Polymorphic Genes of Major Effect: Consequences for Variation,Selection and Evolution in Arabidopsis thaliana

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus can have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure—in particular the genetic correlations between branch number and flowering time traits—and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we find that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices.THE structure of the genetic variation that underlies phenotypic traits has important consequences for understanding the evolution of quantitative traits (Fisher 1930; Lande 1979; Bulmer 1980; Kimura 1983; Orr 1998; Agrawal et al. 2001). Despite the infinitesimal model''s allure and theoretical tractability (see Orr and Coyne 1992; Orr 1998, 2005a,b for reviews of its influence), evidence has accumulated from several sources (artificial selection experiments, experimental evolution, and QTL mapping) to suggest that genes of major effect often contribute to quantitative traits. Thus, the frequency and role of genes of major effect in evolutionary quantitative genetics have been a subject of intense debate and investigation for close to 80 years (Fisher 1930; Kimura 1983; Orr 1998, 2005a,b). Beyond the conceptual implications, the prevalence of major-effect loci also affects our ability to determine the genetic basis of adaptations and species differences (e.g., Bradshaw et al. 1995, 1998).Although the existence of genes of major effect is no longer in doubt, we still lack basic empirical data on how segregating variation at such genes affects key components of evolutionary process (but see Carrière and Roff 1995). In other words, How does polymorphism at genes of major effect alter patterns of genetic variation and covariation, natural selection, and the likely response to selection? The lack of data stems, in part, from the methods used to detect genes of major effect: experimental evolution (e.g., Bull et al. 1997; Zeyl 2005) and QTL analysis (see Erickson et al. 2004 for a review) often detect such genes retrospectively after they have become fixed in experimental populations or the species pairs used to generate the mapping population. The consequences of polymorphism at these genes on patterns of variation, covariation, selection, and the response to selection—which can be transient (Agrawal et al. 2001)—are thus often unobserved.A partial exception to the absence of data on the effects of major genes comes from artificial selection experiments, in which a substantial evolutionary response to selection in the phenotype after a plateau is often interpreted as evidence for the fixation of a major-effect locus (Frankham et al. 1968; Yoo 1980a,b; Frankham 1980; Shrimpton and Robertson 1988a,b; Caballero et al. 1991; Keightley 1998; see Mackay 1990 and Hill and Caballero 1992 for reviews). However, many of these experiments report only data on the selected phenotype (e.g., bristle number) or, alternatively, the selected phenotype and some measure of fitness (e.g., Frankham et al. 1968, Yoo 1980b; Caballero et al. 1991; Mackay et al. 1994; Fry et al. 1995; Nuzhdin et al. 1995; Zur Lage et al. 1997), making it difficult to infer how a mutation will affect variation, covariation, selection, and evolutionary responses for a suite of traits that might affect fitness themselves. One approach is to document how variation at individual genes of major effect affects the genetic variance–covariance matrix (“G matrix”; Lande 1979), which represents the additive genetic variance and covariance between traits.Although direct evidence for variation at major-effect genes altering patterns of genetic variation, covariation, and selection is rare, there is abundant evidence for the genetic mechanisms that could produce these dynamics. A gene of major effect could have these consequences due to any of at least three genetic mechanisms: (1) pleiotropy, where a gene of major effect influences several traits, including potentially fitness, simultaneously, (2) physical linkage or linkage disequilibrium (LD), in which a gene of major effect is either physically linked or in LD with other genes that influence other traits under selection, and (3) epistasis, in which the allele present at a major-effect gene alters the phenotypic effect of other loci and potentially phenotypes under selection. Evidence for these three evolutionary genetic mechanisms leading to changes in suites of traits comes from a variety of sources, including mutation accumulation experiments (Clark et al. 1995; Fernandez and Lopez-Fanjul 1996), mutation induction experiments (Keightley and Ohnishi 1998), artificial selection experiments (Long et al. 1995), and transposable element insertions (Rollmann et al. 2006). For pleiotropy in particular, major-effect genes that have consequences on several phenotypic traits are well known from the domestication and livestock breeding literature [e.g., myostatin mutations in Belgian blue cattle and whippets (Arthur 1995; Grobet et al. 1997; Mosher et al. 2007), halothane genes in pigs (Christian and Rothschild 1991; Fujii et al. 1991), and Booroola and Inverdale genes in sheep (Amer et al. 1999; Visscher et al. 2000)]. While these data suggest that variation at major-effect genes could—and probably does—influence variation, covariation, and selection on quantitative traits, data on the magnitude of these consequences remain lacking.Recombinant inbred line (RIL) populations are a promising tool for investigating the influence of major-effect loci. During advancement of the lines from F₂''s to RILs, alternate alleles at major-effect genes (and most of the rest of the genome) will be made homozygous, simplifying comparisons among genotypic classes. Because of the high homozygosity, individuals within RILs are nearly genetically identical, facilitating phenotyping of many genotypes under a range of environments. In addition, because of recombination, alternative alleles are randomized across genetic backgrounds—facilitating robust comparisons between sets of lines differing at a major-effect locus.Here we investigate how polymorphism at an artificially induced mutation, the erecta locus in Arabidopsis thaliana, affects the magnitude of these important evolutionary genetic parameters under ecologically realistic field conditions. We use the Landsberg erecta (Ler) × Columbia (Col) RIL population of A. thaliana to examine how variation at a gene of major effect influences genetic variation, covariation, and selection on quantitative traits in a field setting. The Ler × Col RIL population is particularly suitable, because it segregates for an artificially induced mutation at the erecta locus, which has been shown to influence a wide variety of plant traits. The Ler × Col population thus allows a powerful test of the effects of segregating variation at a gene—chosen a priori—with numerous pleiotropic effects. The ERECTA gene is a leucine-rich receptor-like kinase (LRR-RLK) (Torii et al. 1996) and has been shown to affect plant growth rates (El-Lithy et al. 2004), stomatal patterning and transpiration efficiency (Masle et al. 2005; Shpak et al. 2005), bacterial pathogen resistance (Godiard et al. 2003), inflorescence and floral organ size and shape (Douglas et al. 2002; Shpak et al. 2003, 2004), and leaf polarity (Xu et al. 2003; Qi et al. 2004).Specifically, we sought to answer the following questions: (1) Is variation at erecta significantly associated with changes to the G matrix? (2) Is variation at erecta associated with changes in natural selection on genetically variable traits? And (3) is variation at erecta associated with significantly different projected evolutionary responses to selection?     

Reproductive Isolation in Hybrid Mice Due to Spermatogenesis Defects at Three Meiotic Stages

Early in the process of speciation, reproductive failures occur in hybrid animals between genetically diverged populations. The sterile hybrid animals are often males in mammals and they exhibit spermatogenic disruptions, resulting in decreased number and/or malformation of mature sperms. Despite the generality of this phenomenon, comparative study of phenotypes in hybrid males from various crosses has not been done, and therefore the comprehensive genetic basis of the disruption is still elusive. In this study, we characterized the spermatogenic phenotype especially during meiosis in four different cases of reproductive isolation: B6-ChrX^MSM, PGN-ChrX^MSM, (B6 × Mus musculus musculus-NJL/Ms) F₁, and (B6 × Mus spretus) F₁. The first two are consomic strains, both bearing the X chromosome of M. m. molossinus; in B6-ChrX^MSM, the genetic background is the laboratory strain C57BL/6J (predominantly M. m. domesticus), while in PGN-ChrX^MSM the background is the PGN2/Ms strain purely derived from wild M. m. domesticus. The last two cases are F₁ hybrids between mouse subspecies or species. Each of the hybrid males exhibited cell-cycle arrest and/or apoptosis at either one or two of three distinct meiotic stages: premeiotic stage, zygotene-to-pachytene stage of prophase I, and metaphase I. This study shows that the sterility in hybrid males is caused by spermatogenic disruptions at multiple stages, suggesting that the responsible genes function in different cellular processes. Furthermore, the stages with disruptions are not correlated with the genetic distance between the respective parental strains.WHEN animals from genetically diverged populations hybridize, complete or partial sterility is often observed in the F₁ hybrids or in their descendants. This phenomenon belonging to postzygotic reproductive isolation accelerates irreversible genetic divergence by preventing free gene flow across the two diverging populations, and thereby plays a pivotal role in speciation. Sexual dimorphism is a general feature of reproductive isolation (Wu and Davis 1993; Laurie 1997; Orr 1997; Kulathinal and Singh 2008). In mammals, impairment is much more severe in males than in females, and in general the heterogametic sex is more sensitive to interspecific and intersubspecific genetic incompatibility. This phenomenon is well known as Haldane''s rule (Haldane 1922; Laurie 1997; Orr 1997).In many animals, the reproductive isolation is caused by spermatogenic disruptions characterized by reduced number of germ cells and small testis size. These animals include Drosophila (Joly et al. 1997), stickleback fish Pungitius (Takahashi et al. 2005), caviomorph rodent Thrichomys (Borodin et al. 2006), house musk shrew Suncus (Borodin et al. 1998), wallaby Petrogale (Close et al. 1996), and genus Mus (Forejt and Iványi 1974; Matsuda et al. 1992; Hale et al. 1993; Yoshiki et al. 1993; Kaku et al. 1995; Gregorová and Forejt 2000; Elliott et al. 2001, 2004; Good et al. 2008). Although reproductive isolation by spermatogenic impairment is a well-known phenomenon, its underlying genetic mechanism and molecular basis have remained elusive. The Dobzhansky–Muller model, which infers that hybrid sterility or inviability is caused by deleterious epistatic interactions between nuclear genes derived from their respective parent species or subspecies (Dobzhansky 1936; Muller 1942), is widely accepted in animals and plants and is also applicable to the sterility of hybrid animals in F₂ or backcross generations, so-called hybrid breakdown, in which the genes causing postzygotic reproductive isolation are partially recessive (Orr 2005).The genetic incompatibility between house mouse subspecies is an ideal animal model for studying the early stage of speciation. Two subspecies of mouse, Mus musculus domesticus and M. m. musculus, diverged from their common ancestor 0.3–1.0 MYA (Yonekawa et al. 1980; Moriwaki 1994; Bonhomme and Guénet 1996; Boursot et al. 1996; Din et al. 1996). M. m. domesticus ranges across western Europe and the Middle East, whereas M. m. musculus ranges throughout eastern Europe and northern Asia (Bonhomme and Guénet 1996). The two subspecies meet in a narrow hybrid zone, which is most likely maintained by a balance between dispersal and selection against hybrids (Hunt and Selander 1973; Bonhomme and Guénet 1996; Payseur et al. 2004). M. m. domesticus also displays reproductive isolation from the Japanese wild mouse, M. m. molossinus, which originated from hybridization of M. m. castaneus and M. m. musculus and its nuclear genome is predominantly derived from M. m. musculus (Yonekawa et al. 1980, 1988; Moriwaki 1994; Sakai et al. 2005). To investigate the reproductive isolation between M. m. domesticus and M. m. molossinus, we previously constructed a consomic strain B6-ChrX^MSM (Oka et al. 2004). This strain has the X chromosome from the MSM/Ms strain, which is derived from M. m. molossinus, in the genetic background of the laboratory strain C57BL/6J (B6), which is predominantly derived from M. m. domesticus (Moriwaki 1994). F₁ hybrid animals between B6 and MSM/Ms strains are fully fertile. On the contrary, B6-ChrX^MSM shows male-specific sterility characterized by a reduced sperm number and dysfunction of the sperm, including abnormal morphology and low motility, indicating that B6-ChrX^MSM is a model of hybrid breakdown in animals (Oka et al. 2004, 2007). Our previous study indicated that the abnormal morphology of the sperm head results from the genetic incompatibility between MSM/Ms-derived X-linked genes and B6 genes on autosomes including chromosomes 1 and 11 (Oka et al. 2007).In this study, to understand the genetic mechanism of reproductive isolation in mice, we first undertook in-depth characterization of phenotype for each B6-ChrX^MSM male especially during meiosis. Meiosis is a special cell division that produces four haploid cells after one round of chromosome replication and two rounds of chromosome segregation. During meiosis, homologous chromosomes pair, synapse, undergo crossing over, and achieve bipolar attachment to the spindle to segregate one set of chromosomes to each daughter cell. Homologous recombination is initiated during the leptotene stage of meiotic prophase I with the formation of DNA double-strand breaks (DSBs), which are repaired immediately during the zygotene stage or after crossing over of homologous chromosomes during the pachytene stage (Roeder 1997; Tarsounas and Moens 2001).During the first wave of spermatogenesis, most mitotic spermatogonia in the B6-ChrX^MSM testes fail to initiate meiotic DNA replication. Some proportion of those spermatogonia that enter into meiosis are again arrested and eliminated by apoptosis at the pachytene stage, resulting in the production of a small number of sperms. We extended the same analysis to three other cases of reproductive isolation. Another consomic strain PGN-ChrX^MSM has an MSM/Ms-derived X chromosome in the genetic background of the PGN2/Ms strain derived from wild mice (M. m. domesticus). PGN-ChrX^MSM males produce a small number of dysfunctional sperms as was the case with B6-ChrX^MSM males, but the former males show apoptosis mainly at metaphase of meiosis I. Furthermore, we examined F₁ hybrid males from intersubspecific cross of (B6 × M. m. musculus-NJL/Ms) and interspecific cross of (B6 × M. spretus). These F₁ hybrid males exhibited apoptosis at metaphase I and at the zygotene-to-pachytene stage of prophase I. As a whole, the postzygotic reproductive isolation in mice is caused by disruptions at a minimum of three different spermatogenic stages.     

Excision of an Active CACTA-Like Transposable Element From DFR2 Causes Variegated Flowers in Soybean [Glycine max (L.) Merr.]

Active endogenous transposable elements, useful tools for gene isolation, have not been reported from any legume species. An active transposable element was suggested to reside in the W4 locus that governs flower color in soybean. Through biochemical and molecular analyses of several revertants of the w4-m allele, we have shown that the W4 locus encodes dihydroflavonol-4-reductase 2 (DFR2). w4-m has arisen through insertion of Tgm9, a 20,548-bp CACTA-like transposable element, into the second intron of DFR2. Tgm9 showed high nucleic acid sequence identity to Tgmt*. Its 5′ and 3′ terminal inverted repeats start with conserved CACTA sequence. The 3′ subterminal region is highly repetitive. Tgm9 carries TNP1- and TNP2-like transposase genes that are expressed in the mutable line, T322 (w4-m). The element excises at a high frequency from both somatic and germinal tissues. Following excision, reinsertions of Tgm9 into the DFR2 promoter generated novel stable alleles, w4-dp (dilute purple flowers) and w4-p (pale flowers). We hypothesize that the element is fractured during transposition, and truncated versions of the element in new insertion sites cause stable mutations. The highly active endogenous transposon, Tgm9, should facilitate genomics studies specifically that relate to legume biology.IN soybean [Glycine max (L.) Merr.], five loci W1, W3, W4, Wm, and Wp control the pigmentations in flowers and hypocotyls (Palmer et al. 2004). Soybean plants with genotype W1_ w3w3 W4_ Wm_ Wp_ produce wild-type purple flowers (Figure 1) and purple hypocotyls. Mutations at the W4 locus in the W1_ background result in altered pigment accumulation patterns in petals and reduced levels of purple pigments in flowers and hypocotyls. Four mutant alleles, w4, w4-m, w4-dp, and w4-p have been mapped to this locus. The w4 allele represents a spontaneous mutation, which produces near-white flowers (Figure 1) and green hypocotyls (Hartwig and Hinson 1962; Groose and Palmer 1991). The w4-m allele was identified from a cross between two experimental breeding lines with white and purple flowers, respectively (Palmer et al. 1989; Weigelt et al. 1990). w4-m is characterized by variegated flowers (Figure 1) and green hypocotyls with purple sectors (Groose et al. 1988).Open in a separate windowFigure 1.—Variation in flower color among soybean lines carrying different W4 alleles.w4-m has been proposed to harbor a class II transposable element (Palmer et al. 1989). Presumably, somatic excision of the putative transposable element results in the variegated (Groose et al. 1988) and germinal excision wild-type phenotypes, purple flowers and purple pigments on hypocotyls (Palmer et al. 1989; Groose et al. 1990). The mutable line carrying w4-m undergoes germinal reversion at a very high frequency, about 6% per generation (Groose et al. 1990). Approximately 1% of the progeny derived from germinal revertants contain new mutations in unlinked loci, presumably resulting from reinsertion of the element (Palmer et al. 1989). For example, female partial-sterile 1 (Fsp1), female partial-sterile 2 (Fsp2), female partial-sterile 3 (Fsp3), and female partial-sterile 4 (Fsp4) were isolated from progenies of germinal revertants with purple flowers and were mapped to molecular linkage groups (MLG) C2, A2, F, and G, respectively (Kato and Palmer 2004). Similarly, 36 male-sterile, female-sterile mutants mapped to the st8 region on MLG J (Kato and Palmer 2003; Palmer et al. 2008a), 24 necrotic root (rn) mutants mapped to the rn locus on MLG G (Palmer et al. 2008b), and three Mdh1-n y20 mutants, mapped to a chromosomal region on MLG H (Palmer et al. 1989; Xu and Palmer 2005b), were isolated among progenies of germinal revertants.In addition to germinal revertants with purple flowers, the w4 mutable line also generated intermediate stable revertants that produce flowers with variable pigment intensities ranging from purple to near-white (Figure 1). Two stable intermediate revertants, w4-dp and w4-p, are allelic to W4. Plants carrying w4-dp or w4-p alleles produce dilute purple flowers or pale flowers, respectively (Figure 1) (Palmer and Groose 1993; Xu and Palmer 2005a).Pigment formation requires two types of genes: structural genes that encode anthocyanin biosynthetic enzymes [e.g., CHS (chalcone synthase), F3H (flavanone 3-hydroxylase), DFR (dihydroflavonol-4-reductase), ANS (anthocyanidin synthase); Figure S1] and regulatory genes that control expression of structural genes (Holton and Cornish 1995). Among the five genes, W1, W3, W4, Wp, and Wm, controlling pigment biosynthesis in soybean, four have been characterized at the molecular level (Figure S1). W1 encodes a flavonoid 5′, 3′-hydroxylase (Zabala and Vodkin 2007). W3 cosegregates with a DFR gene, Wp encodes a flavonone 3-hydroxylase (F3H), and Wm encodes a flavonol synthase (FLS) (Fasoula et al. 1995; Zabala and Vodkin 2005; Takahashi et al. 2007).Nine CACTA-type class II transposable elements, Tgm1, Tgm2, Tgm3, Tgm4, Tgm5, Tgm6, Tgm7, Tgm-Express1, and Tgmt*, have been reported in soybean (Rhodes and Vodkin 1988; Zabala and Vodkin 2005, 2008). Tgm-Express1 causes mutation in Wp (Zabala and Vodkin 2005) and Tgmt* ({"type":"entrez-nucleotide","attrs":{"text":"EU190440","term_id":"158514866","term_text":"EU190440"}}EU190440) in T that encodes a flavonoid 3′ hydroxylase (F3′H) (Zabala and Vodkin 2003, 2008). The objectives of the present study were to characterize the W4 locus and then investigate whether the w4-m allele harbors an active transposable element. Our results showed that a CACTA-like transposable element located in a dihydroflavonol-4-reductase gene causes variegated flower phenotype in soybean.     

Exact Tests for Hardy–Weinberg Proportions

Exact conditional tests are often required to evaluate statistically whether a sample of diploids comes from a population with Hardy–Weinberg proportions or to confirm the accuracy of genotype assignments. This requirement is especially common when the sample includes multiple alleles and sparse data, thus rendering asymptotic methods, such as the common χ²-test, unreliable. Such an exact test can be performed using the likelihood ratio as its test statistic rather than the more commonly used probability test. Conceptual advantages in using the likelihood ratio are discussed. A substantially improved algorithm is described to permit the performance of a full-enumeration exact test on sample sizes that are too large for previous methods. An improved Monte Carlo algorithm is also proposed for samples that preclude full enumeration. These algorithms are about two orders of magnitude faster than those currently in use. Finally, methods are derived to compute the number of possible samples with a given set of allele counts, a useful quantity for evaluating the feasibility of the full enumeration procedure. Software implementing these methods, ExactoHW, is provided.WHEN studying the genetics of a population, one of the first questions to be asked is whether the genotype frequencies fit Hardy–Weinberg (HW) expectations. They will fit HW if the population is behaving like a single randomly mating unit without intense viability selection acting on the sampled loci. In addition, testing for HW proportions is often used for quality control in genotyping, as the test is sensitive to misclassifications or undetected null alleles. Traditionally, geneticists have relied on test statistics with asymptotic χ²-distributions to test for goodness-of-fit with respect to HW proportions. However, as pointed out by several authors (Elston and Forthofer 1977; Emigh 1980; Louis and Dempster 1987; Hernandez and Weir 1989; Guo and Thompson 1992; Chakraborty and Zhong 1994; Rousset and Raymond 1995; Aoki 2003; Maiste and Weir 2004; Wigginton et al. 2005; Kang 2008; Rohlfs and Weir 2008), these asymptotic tests quickly become unreliable when samples are small or when rare alleles are involved. The latter situation is increasingly common as techniques for detecting large numbers of alleles become widely used. Moreover, loci with large numbers of alleles are intentionally selected for use in DNA identification techniques (e.g., Weir 1992). The result is often sparse-matrix data for which the asymptotic methods cannot be trusted.A solution to this problem is to use an exact test (Levene 1949; Haldane 1954) analogous to Fisher''s exact test for independence in a 2 × 2 contingency table and its generalization to rectangular tables (Freeman and Halton 1951). In this approach, one considers only potential outcomes that have the same allele frequencies as observed, thus greatly reducing the number of outcomes that must be analyzed. One then identifies all such outcomes that deviate from the HW null hypothesis by at least as much the observed sample. The total probability of this subset of outcomes, conditioned on HW and the observed allele frequencies, is then the P-value of the test. When it is not possible to enumerate all outcomes, it is still feasible to approximate the P-value by generating a large random sample of tables.The exact HW test has been used extensively and eliminates the uncertainty inherent in the asymptotic methods (Emigh 1980; Hernandez and Weir 1989; Guo and Thompson 1992; Rousset and Raymond 1995). However, there are two difficulties with the application of this method and its interpretation, both of which are addressed in this report.The first issue is the question of how one decides which of the potential outcomes are assigned to the subset that deviates from HW proportions by as much as or more than the observed sample. If the alternative hypothesis is specifically an excess or a dearth of homozygotes, then the tables can be ordered by Rousset and Raymond''s (1995) U-score or, equivalently, by Robertson and Hill''s (1984) minimum-variance estimator of the inbreeding coefficient. However, when no specific direction of deviation from HW is suspected, then there are several possible test statistics that can be used (Emigh 1980). These include the χ²-statistic, the likelihood ratio (LR), and the conditional probability itself. The last option is by far the most widely used (Elston and Forthofer 1977; Louis and Dempster 1987; Chakraborty and Zhong 1994; Weir 1996; Wigginton et al. 2005) and implemented in the GENEPOP software package (Rousset 2008). The idea of using the null-hypothesis probability as the test statistic was originally suggested in the context of rectangular contingency tables (Freeman and Halton 1951), but this idea has been criticized for its lack of discrimination between the null hypothesis and alternatives (Gibbons and Pratt 1975; Radlow and Alf 1975; Cressie and Read 1989). For example, suppose a particular sample was found to have a very low probability under the null hypothesis of HW. Such a result would usually tend to argue against the population being in HW equilibrium. However, if this particular outcome also has a very low probability under even the best-fitting alternative hypothesis, then it merely implies that a rare event has occurred regardless of whether the population is in random-mating proportions. The first part of this report compares the use of probability vs. the likelihood ratio as the test statistic in HW exact tests. Reasons for preferring the likelihood ratio are presented.The second difficulty in performing HW exact tests is the extensive computation needed for large samples when multiple alleles are involved. In this report I present a new algorithm for carrying out these calculations. This method adapts some of the techniques originally developed for rectangular contingency tables in which each possible outcome is represented as a path through a lattice-like network (Mehta and Patel 1983). Unlike the loop-based method currently in use (Louis and Dempster 1987), the new algorithm uses recursion and can be applied to any number of alleles without modification. In addition, it improves the efficiency by about two orders of magnitude, thus allowing the full enumeration procedure to be applied to larger samples and with greater numbers of alleles.The recursion algorithm has been tested successfully on samples with as many as 20 alleles when most of those alleles are rare. However, there are still some samples for which a complete enumeration is not practical. For example, the data from the human Rh locus in Figure 1D would require examining 2 × 10⁵⁶ tables (see below). For such cases a Monte Carlo approach must be used (Guo and Thompson 1992). Several improvements to the method of independent random tables are suggested here to make that approach practical for even the largest of realistic samples, thus eliminating the need for the less-accurate Markov chain approach.Open in a separate windowFigure 1.—Sample data sets: examples that have been used in previous discussions of exact tests for HW proportions. For each data set, a triangular matrix of genotype counts is shown next to the vector of allele counts. (A) From Table 2, bottom row, of Louis and Dempster (1987). (B) From Figure 2 of Guo and Thompson (1992). (C) From the documentation included with the GENEPOP software package (Rousset 2008). (D) From Figure 5 of Guo and Thompson (1992).Finally, I address the problem of determining the number of tables of genotype counts corresponding to a given set of allele counts. This number is needed for determining whether the exact test can be performed by full enumeration. Previously, this number could not be obtained except by actually carrying out the complete enumeration.The methods described are implemented in a software package, ExactoHW, for MacOS X10.5 or later. It is available in compiled form (supporting information, File S1) or as source code for academic use on request from the author.     

Parallel Genetic and Proteomic Screens Identify Msps as a CLASP–Abl Pathway Interactor in Drosophila

Regulation of cytoskeletal structure and dynamics is essential for multiple aspects of cellular behavior, yet there is much to learn about the molecular machinery underlying the coordination between the cytoskeleton and its effector systems. One group of proteins that regulate microtubule behavior and its interaction with other cellular components, such as actin-regulatory proteins and transport machinery, is the plus-end tracking proteins (MT+TIPs). In particular, evidence suggests that the MT+TIP, CLASP, may play a pivotal role in the coordination of microtubules with other cellular structures in multiple contexts, although the molecular mechanism by which it functions is still largely unknown. To gain deeper insight into the functional partners of CLASP, we conducted parallel genetic and proteome-wide screens for CLASP interactors in Drosophila melanogaster. We identified 36 genetic modifiers and 179 candidate physical interactors, including 13 that were identified in both data sets. Grouping interactors according to functional classifications revealed several categories, including cytoskeletal components, signaling proteins, and translation/RNA regulators. We focused our initial investigation on the MT+TIP Minispindles (Msps), identified among the cytoskeletal effectors in both genetic and proteomic screens. Here, we report that Msps is a strong modifier of CLASP and Abl in the retina. Moreover, we show that Msps functions during axon guidance and antagonizes both CLASP and Abl activity. Our data suggest a model in which CLASP and Msps converge in an antagonistic balance in the Abl signaling pathway.COORDINATION of cytoskeletal dynamics is an essential process for the regulation of virtually all aspects of cellular behavior including cell shape changes, cell division, and cell motility (e.g., Rodriguez et al. 2003; Kodama et al. 2004). Not only is the cytoskeletal system coordinated with numerous cellular pathways to control cell behavior, it also functions as a central organizing scaffold for multiple effector protein complexes downstream of signaling pathways and cellular processes such as intracellular transport. Yet, little is known regarding the molecular machinery that governs the integrated coordination of various cytoskeletal components. Evidence suggests that the microtubule (MT) plus-end tracking protein CLASP [cytoplasmic linker protein (CLIP)-associated protein], which has been implicated in mitotic spindle formation (Inoue et al. 2000, 2004) and in linking MT ends to other cell structures such as the cell cortex and kinetochore (Akhmanova et al. 2001; Maiato et al. 2003; Mimori-Kiyosue et al. 2005; Reis et al. 2009), may play a pivotal role in the overall coordination of cytoskeletal networks. Not only does it affect MT dynamics, but CLASP may function as an actin-MT crosslinker as well, as it possesses actin-binding activity (Tsvetkov et al. 2007) and CLASP-bound microtubules appear to track along F-actin bundles in growth cones (Lee et al. 2004).We previously showed that CLASP functions downstream of Abelson (Abl) nonreceptor tyrosine kinase (Lee et al. 2004), which is a key signaling molecule that modulates the cytoskeleton downstream of numerous cell surface receptor inputs and plays essential roles in various contexts including cell motility and human disease (Van Etten 1999; Moresco and Koleske 2003; Bradley and Koleske 2009). While most cytoskeletal-related studies of Abl have focused on its regulation of actin dynamics (Lanier and Gertler 2000; Wills et al. 2002; Hernandez et al. 2004; Bradley and Koleske 2009), few studies have examined its MT effectors such as CLASP and how they may be involved in the coordination of both cytoskeletal networks. Additionally, we previously reported that Drosophila CLASP is necessary for accurate embryonic axon guidance at the central nervous system (CNS) midline where conserved guidance factors (Netrins and Slits) control growth cone navigation (Lee et al. 2004). In embryonic axons, we found that CLASP is required for Abl function (Lee et al. 2004); however, additional partner proteins required for CLASP activity during axon guidance are largely unknown.Recent studies of CLASP function focusing on cell culture and imaging have identified several CLASP-binding proteins, such as additional MT+TIPs (CLIP-170 and EB1) (Akhmanova et al. 2001; Mimori-Kiyosue et al. 2005) and cell cortex-associated proteins (LL5beta and ELKS) (Lansbergen et al. 2006). While investigation of the detailed interactions and functional significance of these types of molecules and their relation to CLASP has been important to understanding the CLASP molecular mechanism, elucidating how CLASP functions in a broader context will require expanding our awareness of the entire CLASP network, or “interactome.” As of yet, there has not been a comprehensive unbiased survey of CLASP functional interactors.A long history of molecular pathway dissection in multiple model systems and biological contexts has shown that genetic and proteomic interactome screens are powerful tools for defining the network of functional partners for any given gene of interest (Xu et al. 1990; Simon et al. 1991; Carthew et al. 1994; Karim et al. 1996; Rebay et al. 2000; St Johnston 2002). Determining the CLASP interaction network can not only define MT+TIP-associated proteins and regulators of MT biology, but it can also reveal new classes of molecules that interact with CLASP. For example, a recent study suggested that CLASPs function as actin-MT crosslinkers because they possess actin-binding activity (Tsvetkov et al. 2007), but few specific actin-binding CLASP interactors have been identified, and the functional relevance of CLASP–actin interaction is still unclear. A systematic approach to define the CLASP interactome has the potential for significantly increasing our ability to understand the CLASP mechanism.Therefore, to expand our knowledge of the CLASP functional mechanism, we have performed a multilevel genetic and proteomic screen for CLASP interactors in Drosophila. The single Drosophila CLASP ortholog has been given multiple names [orbit/multiple asters (MAST)/chromosome bows (chb)] (Fedorova et al. 1997; Inoue et al. 2000; Lemos et al. 2000), but here, we refer to it as CLASP. Our screen has identified novel and specific partners in several functional categories including cytoskeletal components, signaling proteins, and the unanticipated class of translation/RNA regulators. To validate the findings of this screen, we focused our initial investigation on the conserved MT+TIP identified among the cytoskeletal effectors in both the genetic and proteomic screens, Minispindles (Msps, ortholog of the human CKAP5 [cytoskeleton associated protein 5)/TOG (tumor overexpressed gene)/Xenopus Xmap215)]. Msps function and regulation of MT stability has been studied previously in the context of the mitotic spindle (Cullen et al. 1999; Lee et al. 2001; Barros et al. 2005) and in centrosomes (Popov et al. 2002; Cassimeris and Morabito 2004), although it has not been shown to functionally interact with CLASP nor play any role in the nervous system.Here, we report that Msps is an in vivo antagonist of CLASP and interacts strongly with Abl. Furthermore, we show that Msps functions during axon guidance. Our data suggest a model in which CLASP and Msps act antagonistically to provide the growth cone with a rapidly adaptable output for Abl-dependent responses to attractive and repulsive guidance cues.     

The Power of the Methods for Detecting Interlocus Gene Conversion

Interlocus gene conversion can homogenize DNA sequences of duplicated regions with high homology. Such nonvertical events sometimes cause a misleading evolutionary interpretation of data when the effect of gene conversion is ignored. To avoid this problem, it is crucial to test the data for the presence of gene conversion. Here, we performed extensive simulations to compare four major methods to detect gene conversion. One might expect that the power increases with increase of the gene conversion rate. However, we found this is true for only two methods. For the other two, limited power is expected when gene conversion is too frequent. We suggest using multiple methods to minimize the chance of missing the footprint of gene conversion.INTERLOCUS (ectopic or nonallelic) gene conversion occurs between paralogous regions such that their DNA sequences are shuffled and homogenized (Petes and Hill 1988; Harris et al. 1993; Goldman and Lichten 1996). As a consequence, the DNA sequences of paralogous genes become similar (i.e., concerted evolution, Ohta 1980; Dover 1982; Arnheim 1983). This homogenizing effect of gene conversion sometimes causes problems in the inference of the evolutionary history of duplicated genes or multigene family. Common misleading inferences include an underestimation of the age of duplicated genes (Gao and Innan 2004; Teshima and Innan 2004). This is largely because the concept of the molecular clock is automatically incorporated in most software of phylogenetic analyses, and those software are frequently applied to multigene families without careful consideration of the potential effect of gene conversion.To understand the evolutionary roles of gene duplication, it is crucial to date each duplication event. To do this, we first need to know precisely the action of gene conversion among the gene family of interest. There have been a number of methods for detecting gene conversion, but their power has not been fully explored. Here, we systematically compare their performance by simulations to provide a guideline on which method works best under what condition. Our simulations show that some methods have a serious problem that causes a misleading interpretation: they do not detect any evidence for gene conversion when the gene conversion rate is too high. Thus, as is always true, lack of evidence is no evidence for absence, and we must be very careful about this effect when analyzing data with those tests, as is demonstrated below.There seem to be four major ideas behind the methods for detecting gene conversion, which are summarized below. A number of methods have been developed to detect interlocus gene conversion, and they belong to one of these four broad categories.

1. Incompatibility between an estimated gene tree and the true duplication history: Figure 1A illustrates a simple situation of a pair of duplicated genes, X and Y, that arose before the speciation event of species A and B. The upper tree of Figure 1A shows a tree representing the true history. When a gene tree is estimated from their DNA sequences, it should be consistent with the true tree when genes X and Y have accumulated mutations independently. Gene conversion potentially violates this relationship. When genes X and Y are subject to frequent gene conversion, the two paralogous genes in each species should be more closely related, resulting in a gene tree illustrated in the bottom tree in Figure 1A. Thus, incongruence between the real tree and an inferred gene tree can provide strong evidence for gene conversion (unless there is no lineage sorting or misinference of the gene tree).Open in a separate windowFigure 1.—Summary of the simulations in the two-species two-locus model. (A) Illustration of the model. (B–E) The power of the four approaches. The average gene conversion tract length (1/q) is assumed to be 100 bp. See Figure S1 for the results with 1/q = 1000 bp.It should be noted that a single gene conversion event usually transfers a short fragment. Consequently, it occasionally happens that incongruence is detected only in a part of the duplicated region. Thus, searching local regions of incongruence has been a well-recognized method for detecting nonvertical evolutionary events such as recombination, gene conversion, and horizontal gene transfer (Farris 1971; Brown et al. 1972), and some computational methods based on this idea have been developed (Balding et al. 1992).
2. Incompatibility of gene trees in different subregions: The idea of (i) can work even without knowing the real history. As mentioned above, incompatibility in the tree shape between different subregions can be evidence for local gene conversion because those subregions should have different histories of gene conversion (Sneath et al. 1975; Stephens 1985). A number of statistical algorithms incorporate this idea (e.g., Jakobsen et al. 1997; McGuire et al. 1997; Weiller 1998).
3. GENECONV: A local gene conversion also leaves its trace in the alignment of sequences. GENECONV is a software developed by Sawyer (1989) to detect such signatures (http://www.math.wustl.edu/∼sawyer/geneconv/). GENECONV looks at an alignment of multiple sequences in a pairwise manner and searches unusually long regions of high identity between the focal pair conditional on the pattern of variable sites in the other sequences, which are candidates of recent gene conversion (a similar idea is also seen in Sneath et al. 1975). The statistical significance is determined by random shuffling of variable sites in the alignment.
4. Shared polymorphism: Suppose polymorphism data are available in both of the duplicated genes. Then, with gene conversion, there could be polymorphisms shared by the two genes, which can be evidence for gene conversion (Innan 2003a). It should be noted that parallel mutations can create shared polymorphism even without gene conversion, but the chance should be very low when the point mutation rate is usually very low. Polymorphism data usually have tremendous amounts of information on very recent events and can be a powerful means to detect gene conversion (e.g., Stephens 1985; Betrán et al. 1997; Innan 2002).

In this study, we investigate and compare the performance of the methods based on these four ideas with simple settings. It should be noted that because our primary focus is on interlocus gene conversion, we ignore methods that can be used for detecting only allelic gene conversion, such as Fearnhead and Donnelly (2001), Hudson (2001), and Gay et al. (2007).     

Targeted Genome Modification in Mice Using Zinc-Finger Nucleases

Homologous recombination-based gene targeting using Mus musculus embryonic stem cells has greatly impacted biomedical research. This study presents a powerful new technology for more efficient and less time-consuming gene targeting in mice using embryonic injection of zinc-finger nucleases (ZFNs), which generate site-specific double strand breaks, leading to insertions or deletions via DNA repair by the nonhomologous end joining pathway. Three individual genes, multidrug resistant 1a (Mdr1a), jagged 1 (Jag1), and notch homolog 3 (Notch3), were targeted in FVB/N and C57BL/6 mice. Injection of ZFNs resulted in a range of specific gene deletions, from several nucleotides to >1000 bp in length, among 20–75% of live births. Modified alleles were efficiently transmitted through the germline, and animals homozygous for targeted modifications were obtained in as little as 4 months. In addition, the technology can be adapted to any genetic background, eliminating the need for generations of backcrossing to achieve congenic animals. We also validated the functional disruption of Mdr1a and demonstrated that the ZFN-mediated modifications lead to true knockouts. We conclude that ZFN technology is an efficient and convenient alternative to conventional gene targeting and will greatly facilitate the rapid creation of mouse models and functional genomics research.CONVENTIONAL gene targeting technology in mice relies on homologous recombination in embryonic stem (ES) cells to target specific gene sequences, most commonly to disrupt gene function (Doetschman et al. 1987; Kuehn et al. 1987; Thomas and Capecchi 1987). Advantages of gene targeting in ES cells are selective target sequence modification, the ability to insert or delete genetic information, and the stability of the targeted mutations through subsequent generations. There are also potential limitations, including limited rates of germline transmission and strain limitations due to lack of conventional ES cell lines (Ledermann 2000; Mishina and Sakimura 2007). Moving the targeted allele from one strain to another requires 10 generations of backcrosses that take 2–3 years. A minimum of 1 year is necessary for backcrossing if speed congenics is applied (Markel et al. 1997).Zinc-finger nucleases (ZFNs) are fusions of specific DNA-binding zinc finger proteins (ZFPs) and a nuclease domain, such as the DNA cleavage domain of a type II endonuclease, FokI (Kim et al. 1996; Smith et al. 1999; Bibikova et al. 2001). A pair of ZFPs provide target specificity, and their nuclease domains dimerize to cleave the DNA, generating double strand breaks (DSBs) (Mani et al. 2005), which are detrimental to the cell if left unrepaired (Rich et al. 2000). The cell uses two main pathways to repair DSBs: high-fidelity homologous recombination and error-prone nonhomologous end joining (NHEJ) (Lieber 1999; Pardo et al. 2009; Huertas 2010). ZFN-mediated gene disruption results from deletions or insertions frequently introduced by NHEJ. Figure 1 illustrates the cellular events following the injection of a pair of ZFNs targeting the mouse Mdr1a (also known as Abcb1a) gene.Open in a separate windowFigure 1.—The ZFN targeting mechanism. ZFN pairs bind to the target site, and FokI endonuclease domain dimerizes and makes a double strand break between the binding sites. If a DSB is repaired so that the wild-type sequence is restored, ZFNs can bind and cleave again. Otherwise, nonhomologous end joining (NHEJ) introduces deletions or insertions, which change the spacing between the binding sites so that ZFNs might still bind but dimerization or cleavage cannot occur. Insertions or deletions potentially disrupt the gene function.ZFNs have been successfully applied to generate genome modifications in plants (Shukla et al. 2009; Townsend et al. 2009), fruit flies (Bibikova et al. 2002), Caenorhabditis elegans (Morton et al. 2006), cultured mammalian cells (Porteus and Baltimore 2003; Santiago et al. 2008), zebrafish (Doyon et al. 2008; Meng et al. 2008), and most recently in rats (Geurts et al. 2009; Mashimo et al. 2010). The technology is especially valuable for rats because rat ES cell lines have only become available recently (Buehr et al. 2008; Li et al. 2008), and successful homologous recombination-mediated genome modification has not been reported. Previously, ENU mutagenesis (Zan et al. 2003) or transposons (Kitada et al. 2007) were the two main methods for generating gene knockout rats, both of which are random approaches and require labor-intensive and time-consuming screens to obtain the desired gene disruptions.Although ES cell-based knockout technology is widely used in mice, ZFN technology offers three advantages: (i) high efficiency; (ii) drastically reduced timeline, similar to that of creating a transgene (Gordon et al. 1980); and (iii) the freedom to apply the technology in various genetic backgrounds. In addition, no exogenous sequences need to be introduced because selection is not necessary.Here, we created the first genome-engineered mice using ZFN technology. Three genes were disrupted in two different backgrounds: Mdr1a, Jag1, and Notch3 in the FVB/N strain and Jag1 also in the C57BL/6 strain. All founders tested transmitted the genetic modifications through the germline.     

The Role of Replication Bypass Pathways in Dicentric Chromosome Formation in Budding Yeast

Gross chromosomal rearrangements (GCRs) are large scale changes to chromosome structure and can lead to human disease. We previously showed in Saccharomyces cerevisiae that nearby inverted repeat sequences (∼20–200 bp of homology, separated by ∼1–5 kb) frequently fuse to form unstable dicentric and acentric chromosomes. Here we analyzed inverted repeat fusion in mutants of three sets of genes. First, we show that genes in the error-free postreplication repair (PRR) pathway prevent fusion of inverted repeats, while genes in the translesion branch have no detectable role. Second, we found that siz1 mutants, which are defective for Srs2 recruitment to replication forks, and srs2 mutants had opposite effects on instability. This may reflect separate roles for Srs2 in different phases of the cell cycle. Third, we provide evidence for a faulty template switch model by studying mutants of DNA polymerases; defects in DNA pol delta (lagging strand polymerase) and Mgs1 (a pol delta interacting protein) lead to a defect in fusion events as well as allelic recombination. Pol delta and Mgs1 may collaborate either in strand annealing and/or DNA replication involved in fusion and allelic recombination events. Fourth, by studying genes implicated in suppression of GCRs in other studies, we found that inverted repeat fusion has a profile of genetic regulation distinct from these other major forms of GCR formation.ALL organisms are prone to large-scale changes (gross chromosomal rearrangements, GCRs) to their genomes that include deletions, inversions, and translocations. These large-scale changes are thought to drive evolutionary events, such as speciation, and contribute to human pathology such as Pelziaeus-Merzbacher syndrome and other genetic disorders (Lee et al. 2007; Stankiewicz and Lupski 2010). Thus, a firm understanding of how cells normally prevent such rearrangements, and how they accumulate, is critical to our understanding of both evolution and pathology.GCRs arise by many different mechanisms, and there is growing evidence that errors during DNA replication are a major source (Myung et al. 2001; Admire et al. 2006; Mizuno et al. 2009). Errors are thought to arise when replication forks encounter “lesions” on the template strand. Lesions can consist of protein complexes bound to DNA or lesions in the DNA itself. Replication forks bypass lesions by several different mechanisms that are still poorly understood (Atkinson and McGlynn 2009; Weinert et al. 2009). We believe that understanding lesion bypass mechanisms is central to understanding both how GCRs are prevented and how they form when lesion bypass mechanisms fail.All lesion bypass pathways utilize sequence homology to restart replication (Atkinson and McGlynn 2009; Weinert et al. 2009). Use of sequence homology during restart may limit the frequency of GCRs, as it lowers the probability of annealing to nonallelic sequences. Repetitive sequences present a problem because lesion bypass at sites near repetitive sequences may lead to annealing of nonallelic sequences and thus to GCR formation (Lemoine et al. 2005; Narayanan et al. 2006; Argueso et al. 2008). Indeed in yeast and in other organisms, GCRs occur frequently in repeat sequences (Dunham et al. 2002; Argueso et al. 2008; Di Rienzi et al. 2009). Some rearrangements do occur between so-called “single-copy sequences” with either no homology or limited homology (microhomologies of 5–9 bp; Myung et al. 2001; Kolodner et al. 2002; Putnam et al. 2005) though evidence suggests these rearrangements occur less frequently than rearrangements between repetitive sequences (Putnam et al. 2009). Interestingly, it has been shown that some genes are required to prevent the fusion of repetitive elements yet have no effect on rearrangements between single-copy sequences (Putnam et al. 2009). Currently it is not clear how these pathways act to suppress repeat-mediated events and why they are not required to prevent rearrangements between single-copy sequences.Our current understanding of the mechanisms underlying GCR formation is mostly derived from assays designed to detect specific changes to yeast chromosomes (Chen and Kolodner 1999; Myung et al. 2001; Huang and Koshland 2003; Lambert et al. 2005; Rattray et al. 2005; Admire et al. 2006; Narayanan et al. 2006; Schmidt et al. 2006; Smith et al. 2007; Pannunzio et al. 2008; Payen et al. 2008; Paek et al. 2009; Mizuno et al. 2009). Previously we reported on GCR formation in the budding yeast Saccharomyces cerevisiae using an assay we developed. We found that a major source of genome instability involves the fusion of nearby inverted repeats (with ∼20–200 bp of sequence homology, separated by 1–5 kb) to form either dicentric or acentric chromosomes (Figure 1D; Paek et al. 2009). We also found that fusion of inverted repeats is general: fusion occurred between inverted repeats at all five different locations tested on four different yeast chromosomes, as well as between synthetic inverted repeats (Paek et al. 2009). Genetic data suggest that these events most likely occur during replication of DNA (Admire et al. 2006). Further genetic analysis suggested that the mechanism of inverted repeat fusion differed from that of direct repeat recombination, in that inverted repeat fusion did not require genes involved in homologous recombination (HR) or single-strand annealing (SSA) pathways (Paek et al. 2009). In addition, fusion events are unlikely to involve double-strand breaks (DSBs), as genes in the nonhomologous end joining (NHEJ) and microhomology-mediated end joining (MMEJ) are not required for fusion events (Paek et al. 2009). Indeed gene knockouts in the HR (RAD52, RAD51, and RAD59), SSA (RAD52 and RAD1) and postreplication repair (PRR) (RAD18) pathways actually increased the frequency of fusion of an inverted repeat on chromosome (Chr) VII (Paek et al. 2009); these pathways normally suppress inverted repeat fusion.Open in a separate windowFigure 1.—Experimental setup for the detection of inverted repeat fusion and chromosome instability. Objects are not drawn to scale. (A) The starting strain has two copies of Chr VII. One copy contains the CAN1 gene, ADE6, ade3, while the other copy is ade6, ADE3. Cells are plated to canavanine, and three types of colonies are formed: (B) Allelic recombinants are round in appearance and are Ade⁺; (C) colonies that form by loss of Chr VII are round in appearance and Ade⁻; and (D) cells that contain unstable dicentric chromosomes form by the fusion of inverted repeats. One specific case of this fusion (the S2/S3 dicentric) is shown within braces. Cells with dicentrics form mixed colonies, which contain allelic recombinants, chromosome loss events, as well as a translocation between D7 and D11. The bar in the S2/S3 repeat represents a fusion junction. (E) The specific dicentric is detected by dicentric primers DP1 and DP2 and (F) a monocentric translocation that is detected with translocation primers TP1 and TP2.To further our previous studies, we analyzed three groups of genes implicated in the maintenance of genome stability. We tested how these genes affect the overall stability of Chr VII, focusing on the fusion of nearby inverted repeats to form a specific dicentric Chr VII and the resolution of the dicentric into a monocentric translocation (which we term the 403–535 translocation; Figure 1, D–F). First, we analyzed several genes in the PRR pathway and found that error-free bypass, but not translesion synthesis, is required for the prevention of inverted repeat fusion. Surprisingly, we found that siz1 mutants, which are defective for Srs2 recruitment to replication forks, and srs2 mutants had opposite effects on instability. This may reflect separate roles for Srs2 in different phases of the cell cycle. Second, we analyzed several mutations in genes that are associated with replication forks. We found that mutants in POL3 (polymerase delta) and MGS1 (encoding a single-strand annealing protein, which binds polymerase delta) significantly reduced the frequency of dicentric formation and allelic recombinants that arise in the checkpoint mutant rad9 (Giot et al. 1997; Hishida et al. 2001; Paek et al. 2009). Finally we studied genes associated with rearrangements involving repeats or single-copy sequences, as well as a subset of mutants involved in recombination. Generally, we find that the mechanisms of nearby inverted repeat fusion are distinct from mechanisms fusing longer repeats or single-copy sequences.     

Epithelial Polarity: Interactions Between Junctions and Apical–Basal Machinery

Epithelial polarity is established and maintained by competition between determinants that define the apical and basolateral domains. Cell–cell adhesion complexes, or adherens junctions, form at the interface of these regions. Mutations in adhesion components as well as apical determinants normally lead to an expansion of the basolateral domain. Here we investigate the genetic relationship between the polarity determinants and adhesion and show that the levels of the adhesion protein Armadillo affect competition. We find that in arm mutants, even a modest reduction in the basolateral component lgl leads to a full apical domain expansion or lgl phenotype. By using an allelic series of Armadillo mutations, we show that there is a threshold at which basolateral expansion can be reversed. Further, in embryos lacking the Wingless signaling component zw3, the same full apical expansion occurs again with only a reduction in lgl. We propose a model where zw3 regulates protein levels of apical and adhesion components and suggest that a reciprocal interaction between junctions and polarity modules functions to maintain stable apical and basolateral domains.A major reason that epithelial cells require apical–basal polarity is to differentiate between the interior of the organism and the external environment. To accomplish this, epithelial cells generate molecularly distinct domains along their plasma membranes: an apical domain that is exposed to the outside, a basolateral domain that contacts the interior, and, in between, an adhesion complex that holds the cell sheet together. In Drosophila embryos, at least three polarity complexes are used to establish and maintain this subcellular organization commonly known as apical–basal cell polarity. On the apical side, the Crumbs (Crb) and Stardust (Std, Pals) proteins form one complex (Jurgens et al. 1984; Tepass et al. 1990; Tepass and Knust 1993; Wodarz et al. 1995; Muller and Wieschaus 1996). The second one is composed of Bazooka (Baz, Par-3), Par-6, and atypical protein kinase C (aPKC) (Wieschaus et al. 1984; Muller and Wieschaus 1996; Wodarz et al. 2000; Hutterer et al. 2004). On the opposite or basolateral side of the cell, Lethal giant larvae (Lgl), Discs large (Dlg), and Scribble (Scrib) determine the basolateral domain of the plasma membrane (Gateff and Schneiderman 1974; Mechler et al. 1985; Woods and Bryant 1989; Bilder and Perrimon 2000). In between the complexes lie the adherens junctions (AJ) composed of E-cadherin, Armadillo (Arm, β-catenin), and α-catenin (Oda et al. 1993, 1994; Peifer et al. 1993; Tepass et al. 1996).In Drosophila embryos, mutations that affect apical components often lead to the crumbs phenotype, where ectodermal cells lose integrity and many die through apoptosis. The surviving cells secrete cuticle in a discontinuous fashion, leaving pieces apparently floating within the eggshell (Tepass et al. 1990; Tanentzapf and Tepass 2003). This phenotype is also seen in embryos deficient for AJ proteins (Oda et al. 1993; Cox et al. 1996; Magie et al. 2002). On the other hand, mutations that affect the basolateral genes display a very different phenotype. Zygotic only (Z) mutants for scrib, lgl, and dlg have a significant maternal mRNA contribution that allows normal embryonic development to proceed. Phenotypes are observed only in larvae, which die with significantly overgrown imaginal discs (Gateff 1978; Bilder and Perrimon 2000). Removal of the maternal mRNA complement, as well as the zygotic contribution (M/Z) through the induction of germline clones, leads to a poorly differentiated and convoluted cuticle with a bubbly appearance (Figure 1) (Bilder et al. 2003; Tanentzapf and Tepass 2003).Open in a separate windowFigure 1.—Schema and cuticles representing wild-type vs. the opposing phenotypes of apical and basolateral expansion. (A) A wild-type cuticle shows rows of denticles separated by naked regions in a highly organized or patterned fashion. The apical determinants localize to the apical surface of cells, establishing the apical domain (green), the basolateral determinants localize to the basolateral surface of cells, establishing the basolateral domain (blue), and the adherens junctions (red) form at the interface between these two opposing regions. (B) The crumbs phenotype is observed when an apical determinant is mutated, causing an expansion of the basolateral domain. (C) The lgl phenotype, or bubble phenotype, is observed when a basolateral determinant is mutated, causing an expansion of the apical domain.These studies led to a comprehensive competition model where apical and basal components opposed each other (Figure 1, schema); however, a strangely neglected topic was the interaction of junctions and the apical and basal determinants. Therefore, we used a genetic approach to investigate the interaction of apical–basal polarity proteins and adherens junctions.     

The Spontaneous Appearance Rate of the Yeast Prion [PSI+] and Its Implications for the Evolution of the Evolvability Properties of the [PSI+] System

Epigenetically inherited aggregates of the yeast prion [PSI+] cause genomewide readthrough translation that sometimes increases evolvability in certain harsh environments. The effects of natural selection on modifiers of [PSI+] appearance have been the subject of much debate. It seems likely that [PSI+] would be at least mildly deleterious in most environments, but this may be counteracted by its evolvability properties on rare occasions. Indirect selection on modifiers of [PSI+] is predicted to depend primarily on the spontaneous [PSI+] appearance rate, but this critical parameter has not previously been adequately measured. Here we measure this epimutation rate accurately and precisely as 5.8 × 10⁻⁷ per generation, using a fluctuation test. We also determine that genetic “mimics” of [PSI+] account for up to 80% of all phenotypes involving general nonsense suppression. Using previously developed mathematical models, we can now infer that even in the absence of opportunities for adaptation, modifiers of [PSI+] are only weakly deleterious relative to genetic drift. If we assume that the spontaneous [PSI+] appearance rate is at its evolutionary optimum, then opportunities for adaptation are inferred to be rare, such that the [PSI+] system is favored only very weakly overall. But when we account for the observed increase in the [PSI+] appearance rate in response to stress, we infer much higher overall selection in favor of [PSI+] modifiers, suggesting that [PSI+]-forming ability may be a consequence of selection for evolvability.THE yeast phenotype [PSI+] is characterized by prion aggregates of the protein Sup35. Cells are in either a [psi−] (normal) or [PSI+] state, depending on the absence or presence of the prion aggregates (Figure 1, a and b). Sup35 prion aggregates replicate in a similar fashion to mammalian prions but are cytoplasmic and, as such, the prion state is cytoplasmically inherited (Wickner et al. 1995).Open in a separate windowFigure 1.—Comparison between the three possible modes ([PSI+], genetic mimic, point mutation revertant) of the expression of 3′-UTR sequences in yeast. (a) The normal [psi−] phenotypic state; (b) the [PSI+] prion causes readthrough and low-level expression of 3′-UTRs across multiple genes, appearing at rate m_PSI; (c) a genetic mimic of [PSI+] such as the sal3-4 mutant of Sup35 (Eaglestone et al. 1999) appearing at rate m_mimic not reversible by the application of guanidine hydrochloride; (d) a point mutation in a single stop codon at rate μ_point, leading to incorporation of formerly 3′-UTR into a single coding sequence. (e) [PSI+] can act as a “stop-gap” mechanism, buying a lineage more time to acquire one or more adaptive stop codon readthrough point mutations. When this genetic assimilation is complete, [PSI+] can revert to [psi−] (Masel and Bergman 2003; Griswold and Masel 2009).When not part of an aggregate, Sup35 helps mediate translation termination in yeast (Stansfield et al. 1995b; Zhouravleva et al. 1995). Sup35 molecules that are incorporated into nonfunctional prion aggregates are presumably not available for translation termination, which can lead to the translation of stop codons by near-cognate tRNAs (Figure 1b) (Tuite and Mclaughlin 1982; Pure et al. 1985; Lin et al. 1986). This partial loss of Sup35 function leads to an increased frequency of readthrough translation of 3′-untranslated regions (3′-UTR) across all genes (Figure 1b). This increase is modest in wild-type yeast, from an average readthrough rate of 0.3% in [psi−] cells up to 1% in [PSI+] cells (Firoozan et al. 1991). Some [PSI+] yeast strains grow faster than [psi−] controls in certain harsh environments, suggesting that readthrough translation of some 3′-UTRs may be adaptive in certain conditions (True and Lindquist 2000; Joseph and Kirkpatrick 2008). This directly shows that [PSI+]-mediated capacitance may increase evolvability in the laboratory. [PSI+]-mediated phenotypes have a complex genetic basis, involving multiple loci (True et al. 2004).As an epigenetically inherited protein aggregate, [PSI+] can easily be lost after some generations (Cox et al. 1980). This returns the lineage to its normal [psi−] state and restores translation fidelity. If a subset of revealed phenotypic variation is adaptive, it may have lost its dependence on [PSI+] by this time (True et al. 2004). This process of genetic assimilation may, for example, involve one or more point mutations in stop codons, increasing readthrough up to 100% (Figure 1e) (Griswold and Masel 2009). This leaves the yeast with a new adaptive trait and with no permanent load of other, deleterious variation.In general, stop codons can be lost either directly through point mutations or indirectly through upstream indels. This leads to novel coding sequence coming from in-frame and out-of-frame 3′-UTRs, respectively. [PSI+] is expected to facilitate only the former, while mutation bias favors the latter. Yeasts show a much higher ratio of in-frame to out-of-frame 3′-UTR incorporation events than mammals do (Giacomelli et al. 2007), confirming a role for [PSI+] in capacitance-mediated evolvability in natural populations.The adaptive evolution both of evolvability in general (Sniegowski and Murphy 2006; Lynch 2007; Pigliucci 2008) and of capacitance in particular (Dickinson and Seger 1999; Wagner et al. 1999; Partridge and Barton 2000; Brookfield 2001; Pal 2001; Meiklejohn and Hartl 2002; Ruden et al. 2003) is highly controversial. In general, any costs of evolvability are borne in the present, while the benefits lie in the future, making it difficult for natural selection to favor an evolvability allele. For example, mutation rates seem to be set according to a trade-off between metabolic cost (favoring higher mutation rates) and the avoidance of deleterious effects (favoring lower mutation rates) (Sniegowski et al. 2000). The fact that mutation creates variation, the ultimate source of evolvability, is merely a fortuitous consequence of the metabolic cost of fidelity.Previous theoretical population genetic studies have, however, suggested that modifier alleles promoting the formation of [PSI+] might, unlike mutator alleles, be favored for their evolvability properties (King and Masel 2007; Masel et al. 2007; Griswold and Masel 2009; Masel and Griswold 2009). These models depend, however, on a number of parameter estimates. In particular, a number of predictions depend on the spontaneous rate of [PSI+] formation (Masel and Griswold 2009).

[PSI+] appearance rates and the fluctuation test:

The most widely cited spontaneous appearance rate for [PSI+] is m_PSI ∼ 10⁻⁷–10⁻⁵, on the basis of experiments by Lund and Cox (1981). This estimate was calculated as the proportion of colonies scored as [PSI+] after growth over multiple generations from a single founding [psi−] clone. If [PSI+] happens to appear in the first generation of growth, this leads to a “jackpot” event with only one switching event, but many [PSI+] colonies. The proportion of colonies scored as [PSI+] therefore yields a systematic overestimation of the [PSI+] appearance rate.Various implementations of the fluctuation test (Luria and Delbrück 1943) can address such effects. The mutation rate experiment is replicated many times using independent populations, and a Luria–Delbrück distribution is fitted to the results across all replicates. In a simulation study, Stewart (1994) examined a number of estimators of the underlying Luria–Delbrück distribution and found that the maximum-likelihood estimator performed the best.Originally developed to study mutation rates, the fluctuation test can also be used for estimating epimutation rates. Fluctuation tests have been used to estimate the rate of gene silencing in Chinese hamster ovary cells (Holliday and Ho 1998) and in the yeast Schizosaccharomyces pombe (Singh and Klar 2002). However, fluctuation tests do not appear to be used routinely for epimutation rate estimates. For example, although the rates of spontaneous appearance and disappearance of [ISP+], a prion-like element in yeast, have been measured using the fluctuation test (Volkov et al. 2002), to the best of our knowledge there are no published estimates of the spontaneous rate of [PSI+] appearance as measured using a fluctuation test. Although results from the fluctuation test can be confounded by reverse epimutation, or back-switching, this is an issue only if the rate of back-switching is very high, e.g., 10⁻¹–10⁻² per generation (Saunders et al. 2003). This is not the case for [PSI+], for which the reverse epimutation rate (loss of [PSI+]) is <2 × 10⁻⁴ (Tank et al. 2007).

Other [PSI+]-like phenotypes, including genetic mimics:

[PSI+] causes partial loss of Sup35 function, leading to elevated rates of translational readthrough at all stop codons (Figure 1b). There are many other spontaneous changes, presumably mutations, that also lead to elevated translational readthrough (Lund and Cox 1981). Mutations that affect readthrough at all stop codons (Figure 1c) (sometimes called “[PSI+]-like”) can be considered as genetic “mimics” because they produce the same phenotype as the Sup35 aggregate, but are generally not epigenetically inherited. A specific example of such a genetic mimic was characterized by Eaglestone et al. (1999), who identified the sal3-4 point mutation in the SUP35 gene. This leads to a defect in the Sup35 protein structure rendering the termination process less efficient (Eaglestone et al. 1999). The sal3-4 mutant can therefore be considered a partial loss-of-function genetic mimic of [PSI+], since it generates the same readthrough phenotype. Translation termination could also potentially be impaired through other point mutations or deletions, for example, in either the SUP35 or the SUP45 gene (Stansfield et al. 1995a) or in a tRNA that mutates to recognize stop codons at a higher rate. The presence of genetic mimics, whose effects are less reversible than those of [PSI+], can affect the evolution of the evolvability properties of the [PSI+] system such as its epimutation rate (Lancaster and Masel 2009). Note that genetic mimics are quite different from much rarer point mutations that convert stop codons into coding sequence (Figure 1d), resulting in readthrough at a single gene rather than multiple genes.Here we performed experiments to obtain accurate and precise estimates of the baseline appearance rates of both [PSI+] and [PSI+]-like phenotypes in permissive laboratory conditions, excluding stop codon point mutations that affect only a single gene. Our estimates are superior to previous estimates, since we use the fluctuation test. We consider the consequences of these estimates for the evolution of the [PSI+] system.     

Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.MUCH effort in genetics has been devoted to revealing the underlying genetic architecture of quantitative or complex traits. Traditionally, the polygenic model has been used extensively to estimate genetic variances and breeding values of natural and breeding populations, where an infinite number of genes is assumed to code for the trait of interest (Bulmer 1971; Falconer and Mackay 1996). The genetic variance of a quantitative trait can be decomposed into an additive part that corresponds to the effects of individual alleles and a part that is nonadditive because of interactions between alleles. Attention has generally been focused on the estimation of additive genetic variance (and heritability), since additive variation is directly proportional to the response of selection via the breeder''s equation (Falconer and Mackay 1996, Chap. 11). However, to estimate additive genetic variation and heritability accurately, it can be important to identify potential nonadditive sources in genetic evaluations (Misztal 1997; Ovaskainen et al. 2008; Waldmann et al. 2008), especially if the pedigree being analyzed contains a large proportion of full-sibs and clones, as these in particular give rise to nonadditive genetic relationships (Lynch and Walsh 1998, pp. 145). The polygenic model using pedigree and phenotypic information, i.e., the animal model (Henderson 1984), has been the model of choice for estimating genetic parameters in breeding and natural populations (Abney et al. 2000; Sorensen and Gianola 2002; O′Hara et al. 2008).Recent breakthroughs in molecular techniques have made it possible to create genome-wide, single nucleotide polymorphism (SNP) maps. These maps have helped to uncover a vast amount of new loci responsible for trait expression and have provided general insights into the genetic architecture of quantitative traits (e.g., Valdar et al. 2006; Visscher 2008; Flint and Mackay 2009). These insights can help when calculating disease risks in humans, when attempting to increase the yield from breeding programs, and when estimating relatedness in conservation programs. High-density SNPs of many species of importance to science and agriculture can now be scored quickly and relatively cheaply, for example, in mice (Valdar et al. 2006), chickens (Muir et al. 2008), and dairy cattle (VanRaden et al. 2009).In the analysis of populations of breeding stock, the inclusion of dense marker data has improved the predictive ability (i.e., reliability) of genetic evaluations compared to the traditional phenotype model, both in simulations (Meuwissen et al. 2001; Calus et al. 2008; Hayes et al. 2009) and when using real data (Legarra et al. 2008; VanRaden et al. 2009; González-Recio et al. 2009). Meuwissen et al. (2001) suggested that the effect of all markers should first be estimated, and then summed, to obtain genomic estimated breeding values (GEBVs). An alternative procedure, where all markers are used to compute the genomic relationship matrix (in place of the additive polygenic relationship matrix) has also been suggested (e.g., Villanueva et al. 2005; VanRaden 2008; Hayes et al. 2009); this matrix is then incorporated into the statistical analysis to estimate GEBVs. A comparison of both procedures (VanRaden 2008) yielded similar estimates of GEBVs in cases where the effect of an individual allele was small. In addition, if not all pedigree members have marker information, a combined relationship matrix derived from both genotyped and ungenotyped individuals could be computed; this has been shown to increase the accuracy of GEBVs (Legarra et al. 2009; Misztal et al. 2009). Another plausible option to incorporate marker information is to use low-density SNP panels within families and to trace the effect of SNPs from high-density genotyped ancestors, as suggested by Habier et al. (2009) and Weigel et al. (2009). However, fast and powerful computer algorithms, which can use the marker information as efficiently as possible in the analysis of quantitative traits, are needed to obtain accurate GEBVs from genome-wide marker data.This study describes the development of an efficient Bayesian method for incorporating general relationships into the genetic evaluation procedure. The method is based on expressing the multivariate normal prior distribution as a product of one-dimensional normal distributions, each conditioned on the descending variables. When evaluating the genetic parameters of natural and breeding populations, high-dimensional distributions are often used as prior distributions of various genetic effects, such as the additive polygenic effect (Wang et al. 1993), multivariate additive polygenic effects (Van Tassell and Van Vleck 1996), and quantitative trait loci (QTL) effects via the identical-by-decent matrix (Yi and Xu 2000). A Bayesian framework is adopted to obtain posterior distributions of all unknown parameters, estimated by using Markov chain Monte Carlo (MCMC) sampling algorithms in the software package WinBUGS (Lunn et al. 2000, 2009). By performing prior calculations in the form of the factorized product of simple univariate conditional distributions, the computational time of the MCMC estimation procedure is reduced considerably. This feature permits rapid inference for both the polygenic model and the genomic relationship model. Moreover, the decomposition allows for inbreeding of varying degree, since the correct genetic covariance structure can be inferred into the analysis. In this article, we test the method on two previously published pedigree data sets: phenotype data from a large pedigree of Scots pine, incorporation of information on both additive and dominance genetic relationships (Waldmann et al. 2008); and genomic information obtained from a genome-wide scan of a simulated animal population (Lund et al. 2009).     

Sex-Linked Inheritance in Macaque Monkeys: Implications for Effective Population Size and Dispersal to Sulawesi

Sex-specific differences in dispersal, survival, reproductive success, and natural selection differentially affect the effective population size (N_e) of genomic regions with different modes of inheritance such as sex chromosomes and mitochondrial DNA. In papionin monkeys (macaques, baboons, geladas, mandrills, drills, and mangabeys), for example, these factors are expected to reduce N_e of paternally inherited portions of the genome compared to maternally inherited portions. To explore this further, we quantified relative N_e of autosomal DNA, X and Y chromosomes, and mitochondrial DNA using molecular polymorphism and divergence information from pigtail macaque monkeys (Macaca nemestrina). Consistent with demographic expectations, we found that N_e of the Y is lower than expected from a Wright–Fisher idealized population with an equal proportion of males and females, whereas N_e of mitochondrial DNA is higher. However, N_e of 11 loci on the X chromosome was lower than expected, a finding that could be explained by pervasive hitchhiking effects on this chromosome. We evaluated the fit of these data to various models involving natural selection or sex-biased demography. Significant support was recovered for natural selection acting on the Y chromosome. A demographic model with a skewed sex ratio was more likely than one with sex-biased migration and explained the data about as well as an ideal model without sex-biased demography. We then incorporated these results into an evaluation of macaque divergence and migration on Borneo and Sulawesi islands. One X-linked locus was not monophyletic on Sulawesi, but multilocus data analyzed in a coalescent framework failed to reject a model without migration between these islands after both were colonized.THE effective size of a population (N_e) determines the relative impact of genetic drift and natural selection on mutations with mild effects on fitness (Charlesworth 2009). Differences in N_e are hypothesized to affect virtually every aspect of genome evolution, including rates of molecular evolution, abundance of introns and transposable elements, and persistence of duplicate genes, and this has important implications for the evolution of complexity via both adaptive and degenerative processes (Lynch 2007). Of relevance are not only the number of different individuals in a population, but also the number of copies of a gene within each individual. In diploid species with separate sexes, sex chromosomes and mitochondrial DNA (mtDNA) differ in copy number from autosomal DNA (aDNA): both sexes have two alleles at autosomal loci whereas in species with male heterogamy, males have one X and one Y chromosome, females have two Xs, and a female/male pair has effectively only one copy of mtDNA due to maternal inheritance. Sex-specific differences in demographic parameters such as migration, adult sex ratio, and variance in reproductive success also affect relative copy number and associated levels of neutral polymorphism at mtDNA, aDNA, the X chromosome (xDNA), and the Y chromosome (yDNA) (Hedrick 2007).The effective population size is the number of individuals in a Wright–Fisher idealized population (Fisher 1930; Wright 1931) that have the same magnitude of genetic drift as an observed population, where ideal individuals are diploid, and have discrete (nonoverlapping) generations, constant population size, and random mating. N_e can be quantified in terms of variance in allele frequency over generations (variance N_e) or variance in inbreeding over time (inbreeding N_e). If population size is constant with random mating, these approaches for quantifying N_e produce identical results (Kimura and Crow 1963; Whitlock and Barton 1997). At mutation–drift equilibrium with an equal number of males and females and a Poisson distributed number of offspring with a mean of two offspring per individual, N_e-aDNA and N_e-xDNA are expected to be four and three times as large, respectively, as N_e-yDNA and N_e-mtDNA; we refer to this as the “ideal expectation with an equal proportion of males and females.”Demography can alter relationships between N_e of different parts of the genome. For example, extreme skew in adult sex ratio can cause N_e of uniparentally inherited portions of the genome to exceed N_e of biparentally inherited portions (Figure 1A; Nunney 1993; Caballero 1994; Hoelzer 1997; Hedrick 2007). With a skewed sex ratio, the more common sex has a higher variance in reproductive success than the rare one, and this causes the overall variance in reproductive success to increase as the sex-ratio bias increases (Nunney 1993). Sex-biased dispersal such as female philopatry also alters relationships between N_e-aDNA, N_e-xDNA, N_e-yDNA, and N_e-mtDNA (Figure 1B), causing N_e of portions of the genome that disperse less to increase (Nei and Takahata 1993; Hoelzer 1997; Wang and Caballero 1999).Open in a separate windowFigure 1.—N_e of aDNA, xDNA, mtDNA, and yDNA as a function of (A) sex ratio skew and (B) the probability of female dispersal. In B, a finite island model of subdivided populations of constant size is assumed with a population size of 10,000 individuals, 10 subpopulations, and a male probability of migration equal to 0.1.At least five factors related to natural selection also can cause the relative N_e of aDNA, xDNA, yDNA, and mtDNA to depart from expectations: (1) very low or absent recombination in mtDNA and a portion of yDNA, (2) haploidy of mtDNA and yDNA, (3) hemizygosity of xDNA in males, (4) sexual selection and differences in gene content, and (5) differences in the rate and variance of mutation. “Selective sweeps” in which an advantageous mutation is fixed by natural selection, reduces N_e of linked sites (Maynard Smith and Haigh 1974) and this can affect the entire mitochondrial genome and nonrecombining portion of the Y chromosome. Nonrecombining portions of yDNA and mtDNA are also affected by stochastic loss of alleles containing the fewest deleterious mutations (“Muller''s ratchet”; Muller 1964; Felsenstein 1974), which results in a gradual decline of fitness of these chromosomes over time. N_e of nonrecombining DNA is further reduced by elimination of variation linked to substantially deleterious mutations (“background selection”; Charlesworth et al. 1993), by interference between linked polymorphisms that impedes fixation of advantageous alleles and extinction of deleterious ones (the “Hill–Robertson effect”; Hill and Robertson 1966; McVean and Charlesworth 2000), and by increased frequency of deleterious mutations linked to advantageous ones during a selective sweep (“genetic hitchhiking”; Rice 1987). Hemizygous X-linked and haploid Y-linked loci in males and mtDNA loci in both sexes are more vulnerable to recessive deleterious mutations because they are not masked by a second allele (Otto and Goldstein 1992). Hemizygosity on the X chromosome can also increase the rate of selective sweeps when advantageous mutations are recessive (Charlesworth et al. 1987). Similarly, these loci are also susceptible to recessive species incompatibilities—a factor that at least partially accounts for Haldane''s rule for hybrid sterility (Haldane 1922; Orr 1997). Sexual selection differentially influences the probability of fixation of mutations depending on mode of inheritance (Wade and Shuster 2004), especially mutations with antagonistic fitness effects between the sexes (Gibson et al. 2002). Additionally, the rate of evolution of animal mtDNA is much higher than aDNA, xDNA, and yDNA (Haag-Liautard et al. 2008) and this presumably contributes to variation in the frequency of nonneutral mutations in different parts of the genome.Differences among N_e of mtDNA, yDNA, xDNA, and aDNA are thought to be particularly pronounced in papionin monkeys (macaques, baboons, geladas, mandrills, drills, and mangabeys). These monkeys have a highly sex-biased adult demography; females form stable philopatric groups of close relatives, whereas males generally change social groups and disperse more widely (Dittus 1975). Often adult sex ratio of papionins is female biased (Dittus 1975; Melnick and Pearl 1987; O''Brien and Kinnard 1997; Okamoto and Matsumura 2001), and males have higher variance in reproductive success than females (Dittus 1975; de Ruiter et al. 1992; Keane et al. 1997; Van Noordwijk and Van Schaik 2002; Widdig et al. 2004). These sex differences predict strong population subdivision of mtDNA with little or no subdivision of aDNA, deep mtDNA coalescence times, and frequent mtDNA paraphyly among species, and discordant genealogical relationships between mtDNA and yDNA—and this has been observed in multiple studies (Melnick and Pearl 1987; Melnick 1988; Melnick and Hoelzer 1992; Melnick et al. 1993; Hoelzer et al. 1994; Evans et al. 1999, 2001, 2003; Tosi et al. 2000, 2002, 2003; Newman et al. 2004). Female philopatry and obligate male migration is a common social system in mammals (Greenwood 1980; Dobson 1982; Johnson 1986), though less so in humans (Seielstad et al. 1998), and molecular variation provides an effective tool for exploring the impact of natural selection and demography on aDNA, the sex chromosomes, and mtDNA (Nachman 1997; Bachtrog and Charlesworth 2002; Stone et al. 2002; Berlin and Ellegren 2004; Hellborg and Ellegren 2004; Wilder et al. 2004; Hammer et al. 2008).We explored the genetic effects of demography and linked selection in structuring sequence polymorphism of a papionin monkey—the macaques—at two levels. We first tested whether levels of polymorphism in aDNA, xDNA, yDNA, and mtDNA in a Bornean population of the pigtail macaque, Macaca nemestrina, match expectations under scenarios involving natural selection and also whether the data might be explained by simple demographic models with sex-specific dispersal or a biased sex ratio. We then explored demography on a larger, inter-island scale by estimating the time of divergence between macaques on Borneo and Sulawesi islands and by testing for evidence of ongoing migration between these islands.     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.