The Effect of Recent Admixture on Inference of Ancient Human Population History

Despite the widespread study of genetic variation in admixed human populations, such as African-Americans, there has not been an evaluation of the effects of recent admixture on patterns of polymorphism or inferences about population demography. These issues are particularly relevant because estimates of the timing and magnitude of population growth in Africa have differed among previous studies, some of which examined African-American individuals. Here we use simulations and single-nucleotide polymorphism (SNP) data collected through direct resequencing and genotyping to investigate these issues. We find that when estimating the current population size and magnitude of recent growth in an ancestral population using the site frequency spectrum (SFS), it is possible to obtain reasonably accurate estimates of the parameters when using samples drawn from the admixed population under certain conditions. We also show that methods for demographic inference that use haplotype patterns are more sensitive to recent admixture than are methods based on the SFS. The analysis of human genetic variation data from the Yoruba people of Ibadan, Nigeria and African-Americans supports the predictions from the simulations. Our results have important implications for the evaluation of previous population genetic studies that have considered African-American individuals as a proxy for individuals from West Africa as well as for future population genetic studies of additional admixed populations.STUDIES of archeological and genetic data show that anatomically modern humans originated in Africa and more recently left Africa to populate the rest of the world (Tishkoff and Williams 2002; Barbujani and Goldstein 2004; Garrigan and Hammer 2006; Reed and Tishkoff 2006; Campbell and Tishkoff 2008; Jakobsson et al. 2008; Li et al. 2008). Given the central role Africa has played in the origin of diverse human populations, understanding patterns of genetic variation and the demographic history of populations within Africa is important for understanding the demographic history of global human populations. The availability of large-scale single-nucleotide polymorphism (SNP) data sets coupled with recent advances in statistical methodology for inferring parameters in population genetic models provides a powerful means of accomplishing these goals (Keinan et al. 2007; Boyko et al. 2008; Lohmueller et al. 2009; Nielsen et al. 2009).It is important to realize that studies of African demographic history using genetic data have come to qualitatively different conclusions regarding important parameters. Some recent studies have found evidence for ancient (>100,000 years ago) two- to fourfold growth in African populations (Adams and Hudson 2004; Marth et al. 2004; Keinan et al. 2007; Boyko et al. 2008). Other studies have found evidence of very recent growth (Pluzhnikov et al. 2002; Akey et al. 2004; Voight et al. 2005; Cox et al. 2009; Wall et al. 2009) or could not reject a model with a constant population size (Pluzhnikov et al. 2002; Voight et al. 2005). It is unclear why studies found such different parameter estimates. However, these studies all differ from each other in the amount of data considered, the types of data used (e.g., SNP genotypes vs. full resequencing), the genomic regions studied (e.g., noncoding vs. coding SNPs), and the types of demographic models considered (e.g., including migration vs. not including migration postseparation of African and non-African populations).Another important way in which studies of African demographic history differ from each other is in the populations sampled. Some studies have focused on genetic data from individuals sampled from within Africa (Pluzhnikov et al. 2002; Adams and Hudson 2004; Voight et al. 2005; Keinan et al. 2007; Cox et al. 2009; Wall et al. 2009), while other studies included American individuals with African ancestry (Adams and Hudson 2004; Akey et al. 2004; Marth et al. 2004; Boyko et al. 2008). While there is no clear correspondence between those studies which sampled native African individuals (as opposed to African-Americans) and particular growth scenarios, it is clear from previous studies that African-American populations do differ from African populations in their recent demographic history. In particular, genetic studies suggest that there is wide variation in the degree of European admixture in most African-American individuals in the United States and that they have, on average, ∼80% African ancestry and 20% European ancestry (Parra et al. 1998; Pfaff et al. 2001; Falush et al. 2003; Patterson et al. 2004; Tian et al. 2006; Lind et al. 2007; Reiner et al. 2007; Price et al. 2009; Bryc et al. 2010). Furthermore, both historical records and genetic evidence suggest that the admixture process began quite recently, within the last 20 generations (Pfaff et al. 2001; Patterson et al. 2004; Seldin et al. 2004; Tian et al. 2006). Recent population admixture can alter patterns of genetic variation in a discernible and predictable way. For example, recently admixed populations will exhibit correlation in allele frequencies (i.e., linkage disequilibrium) among markers that differ in frequency between the parental populations. This so-called admixture linkage disequilibrium (LD) (Chakraborty and Weiss 1988) can extend over long physical distances (Lautenberger et al. 2000) and decays exponentially with time the since the admixture process began (i.e., recently admixed populations typically exhibit LD over a longer physical distance than anciently admixed populations).While it is clear that African-American populations have a different recent demographic history than do African populations from within Africa and that admixture tracts can be identified in admixed individuals (Falush et al. 2003; Patterson et al. 2004; Tang et al. 2006; Sankararaman et al. 2008a,b; Price et al. 2009; Bryc et al. 2010), the effect that admixture has on other patterns of genetic variation remains unclear. For example, Xu et al. (2007) found similar LD decay patterns when comparing African-American and African populations. It is also unclear whether the recent admixture affects our ability to reconstruct ancient demographic events (such as expansions that predate the spread of humans out of Africa) from whole-genome SNP data. Most studies of demographic history have summarized the genome-wide SNP data by allele frequency or haplotype summary statistics. If these summary statistics are not sensitive to the recent European admixture, then the African-American samples may yield estimates of demographic parameters that are close to the true demographic parameters for the ancestral, unsampled, African populations. This would suggest that the differences in growth parameter estimates obtained from African populations cannot be explained by certain studies sampling African-American individuals and others sampling African individuals from within Africa. However, if these statistics are sensitive to recent admixture, then they may give biased estimates of growth parameters.Here, we examine the effect of recent admixture on the estimation of population demography. In particular, we estimate growth parameters from simulated data sets using SNP frequencies as well as a recently developed haplotype summary statistic (Lohmueller et al. 2009). We compare the demographic parameter estimates made from the admixed and nonadmixed populations and find that some parameter estimates are qualitatively similar between the two populations when inferred using allele frequencies. Inferences of growth using haplotype-based approaches appear to be more sensitive to recent admixture than inferences based on SNP frequencies. We discuss implications that our results have for interpreting studies of demography in admixed populations.     

The Drosophila Claudin Kune-kune Is Required for Septate Junction Organization and Tracheal Tube Size Control

The vertebrate tight junction is a critical claudin-based cell–cell junction that functions to prevent free paracellular diffusion between epithelial cells. In Drosophila, this barrier is provided by the septate junction, which, despite being ultrastructurally distinct from the vertebrate tight junction, also contains the claudin-family proteins Megatrachea and Sinuous. Here we identify a third Drosophila claudin, Kune-kune, that localizes to septate junctions and is required for junction organization and paracellular barrier function, but not for apical-basal polarity. In the tracheal system, septate junctions have a barrier-independent function that promotes lumenal secretion of Vermiform and Serpentine, extracellular matrix modifier proteins that are required to restrict tube length. As with Sinuous and Megatrachea, loss of Kune-kune prevents this secretion and results in overly elongated tubes. Embryos lacking all three characterized claudins have tracheal phenotypes similar to any single mutant, indicating that these claudins act in the same pathway controlling tracheal tube length. However, we find that there are distinct requirements for these claudins in epithelial septate junction formation. Megatrachea is predominantly required for correct localization of septate junction components, while Sinuous is predominantly required for maintaining normal levels of septate junction proteins. Kune-kune is required for both localization and levels. Double- and triple-mutant combinations of Sinuous and Megatrachea with Kune-kune resemble the Kune-kune single mutant, suggesting that Kune-kune has a more central role in septate junction formation than either Sinuous or Megatrachea.EPITHELIA are essential for separating physiologically distinct body compartments and regulating trafficking between them. For proper function, it is imperative that epithelia maintain effective barriers against free paracellular diffusion. To this end, epithelial cells contain occluding junctions, which regulate paracellular permeability. In vertebrates, this is accomplished by tight junctions (TJ), structures that are characterized by regions of close membrane apposition between adjacent cells known as “kissing points” (Tsukita and Furuse 2002). While the TJ is made up of at least 40 different components (Schneeberger and Lynch 2004), the core proteins responsible for the paracellular barrier are the claudins (Angelow et al. 2008).Claudins are four-transmembrane domain proteins that form homo- and heterophilic interactions within the same cell (Furuse et al. 1999; Blasig et al. 2006) and with claudins in adjacent cells (Furuse et al. 1999), thereby establishing the paracellular seal. There are 24 members of the claudin family in mammals, many of which display distinct, tissue-specific expression patterns (Kiuchi-Saishin et al. 2002; Angelow et al. 2008). Mutations in several claudins can cause significant paracellular permeability defects in mice. For example, mutations in claudin-14 increase TJ permeability in the organ of Corti and cause deafness (Ben-Yosef et al. 2003), while loss of claudin-1 compromises epidermal barrier function (Furuse et al. 2002).In Drosophila, primary (ectodermally derived) epithelia lack discernable TJs and instead use pleated septate junctions (SJ) for the paracellular barrier (Baumgartner et al. 1996; Lamb et al. 1998; Genova and Fehon 2003; Paul et al. 2003). However, despite sharing a common barrier function, vertebrate TJs and invertebrate SJs differ in several ways. While vertebrate TJs are positioned apical to adherens junctions (AJ) and contain conserved apical polarity proteins, SJs are basal to AJs and contain conserved basolateral polarity proteins (reviewed in Tepass 2003; Wu and Beitel 2004). In addition, SJs do not contain kissing points, but rather ladder-like septa that span the intermembrane space (Lane and Swales 1982; Tepass and Hartenstein 1994).Beyond their general epithelial barrier function, SJs are also required for several tissue-specific processes. Glial cells, for example, ensheath nerve fibers and use SJs to maintain the blood–brain barrier (Auld et al. 1995; Baumgartner et al. 1996; Schwabe et al. 2005). In the embryonic tracheal system, SJs are required for the apical secretion of the lumenal matrix modifying proteins, Vermiform (Verm) and Serpentine (Serp), which act through undefined pathways to restrict tube length (Wang et al. 2006). This secretory pathway appears to be specific for Verm and Serp, since other apical proteins are secreted normally in SJ mutants. SJ proteins have also been shown to play a role in morphogenesis of the heart tube, even though this tissue lacks typical SJ septa (Yi et al. 2008).Although SJs have clear differences from vertebrate TJs, SJs contain at least two claudins, Megatrachea (Mega) and Sinuous (Sinu), both of which are required for the paracellular barrier (Behr et al. 2003; Wu et al. 2004; Stork et al. 2008). In this article, we identify a third claudin, Kune-kune (Kune), that is an integral SJ protein. Like the other claudins, Kune is required for maintaining epithelial paracellular barrier and tracheal tube size control and is not required for apical-basal polarity. We also find that, of all three characterized claudins, Kune has a more severe SJ phenotype, suggesting that it is a more central player in SJ organization and function than previously characterized Drosophila claudins.     

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?     

In Planta Mutagenesis Determines the Functional Regions of the Wheat Puroindoline Proteins

In planta analysis of protein function in a crop plant could lead to improvements in understanding protein structure/function relationships as well as selective agronomic or end product quality improvements. The requirements for successful in planta analysis are a high mutation rate, an efficient screening method, and a trait with high heritability. Two ideal targets for functional analysis are the Puroindoline a and Puroindoline b (Pina and Pinb, respectively) genes, which together compose the wheat (Triticum aestivum L.) Ha locus that controls grain texture and many wheat end-use properties. Puroindolines (PINs) together impart soft texture, and mutations in either PIN result in hard seed texture. Studies of the PINs'' mode of action are limited by low allelic variation. To create new Pin alleles and identify critical function-determining regions, Pin point mutations were created in planta via EMS treatment of a soft wheat. Grain hardness of 46 unique PIN missense alleles was then measured using segregating F₂:F₃ populations. The impact of individual missense alleles upon PIN function, as measured by grain hardness, ranged from neutral (74%) to intermediate to function abolishing. The percentage of function-abolishing mutations among mutations occurring in both PINA and PINB was higher for PINB, indicating that PINB is more critical to overall Ha function. This is contrary to expectations in that PINB is not as well conserved as PINA. All function-abolishing mutations resulted from structure-disrupting mutations or from missense mutations occurring near the Tryptophan-rich region. This study demonstrates the feasibility of in planta functional analysis of wheat proteins and that the Tryptophan-rich region is the most important region of both PINA and PINB.NATURAL selection has captured a relatively small subset of potentially useful protein sequences. Unraveling the critical features of proteins via understanding the process of their evolution is a powerful approach for proteins present in many diverse species (Bashford et al. 1987; Hampsey et al. 1988). However, this approach is not feasible for the wheat puroindolines (PINs) that are present only in hexaploid wheat and related species (Massa and Morris 2006). The PINs are unique in structure in having a tryptophan-rich domain and are members of the protease inhibitor/seed storage/lipid transfer protein family (PF00234) (Finn et al. 2008).The tryptophan-rich domain has been hypothesized to control PIN function (Giroux and Morris 1997), but there is no unbiased direct evidence for this since previous studies have focused on the tryptophan box alone (Evrard et al. 2008). A nonbiased approach would consist of random mutagenesis followed by functional analysis (Bowie et al. 1990). This approach has been used extensively for proteins that can be expressed in vitro using either random (Tarun et al. 1998; Guo et al. 2004; Smith and Raines 2006; Georgelis et al. 2007) or site-directed mutations (Miyahara et al. 2008; Osmani et al. 2008). However, functional analysis of many plant proteins in vitro may not be comparable to in planta analysis. In the case of puroindolines, there is no in vitro assay that properly mimics the synergistic binding of PINA and PINB to starch granules or is as easy to measure as grain hardness. Therefore, creation and analysis of a large number of new alleles in wheat in planta is an ideal approach to dissect PIN function.The absence of high-throughput transformation and/or functional screening methods in most crop plants is the largest obstacle in the way of in planta protein functional analysis. However, high-throughput in vitro random or targeted mutagenesis followed by functional analysis has been demonstrated in Arabidopsis thaliana (Dunning et al. 2007) and Nicotiana benthamiana (Boter et al. 2007). Traditional in planta mutagenesis followed by analysis of loss-of-function mutations has been used to clone unknown genes (Xiong et al. 2001) or to define function for candidate genes (Haralampidis et al. 2001; Qi et al. 2006). A high-throughput in planta functional approach for PINA and PINB seems attractive for three reasons. First, the EMS mutation rate in wheat is higher than in any other plant (Slade et al. 2005; Feiz et al. 2009a). Second, PINs control the vast majority of variation in grain hardness (Campbell et al. 1999). Finally, a small-scale preliminary study indicated the feasibility of this approach (Feiz et al. 2009a).PINA and PINB are cysteine-rich proteins unique in having a tryptophan-rich domain (Blochet et al. 1993) and together compose the wheat Hardness (Ha) locus (Giroux and Morris 1998; Wanjugi et al. 2007a). Ha is located on chromosome 5DS and is the major determinant of wheat endosperm texture (Mattern et al. 1973; Law et al. 1978; Campbell et al. 1999). Soft texture (Ha) results when both Pin genes are wild type (Pina-D1a, Pinb-D1a) while hard texture (ha) results from mutations in either Pin (Giroux and Morris 1997, 1998). Transgenic studies in rice (Krishnamurthy and Giroux 2001), wheat (Beecher et al. 2002; Martin et al. 2006), and corn (Zhang et al. 2009) have demonstrated that Pin mutations are causative to hard grain texture. PINA and PINB are not functionally interchangeable and control grain hardness via cooperative binding to starch granules (Hogg et al. 2004; Swan et al. 2006; Wanjugi et al. 2007a; Feiz et al. 2009b). PIN binding to starch granules is mediated by polar lipids (Greenblatt et al. 1995) and PIN abundance is correlated with seed polar lipid content (Feiz et al. 2009b). Variation in PIN function affects grain hardness along with nearly all end product quality traits (Hogg et al. 2005; Martin et al. 2007, 2008; Wanjugi et al. 2007b; Feiz et al. 2008). Determining PINs'' function-determining regions could lead to greater knowledge of their mode of action and to wheat quality improvements. Current PIN functional analyses have been limited to in vitro tests of binding to each other (Ziemann et al. 2008) or to yeast membranes (Evrard et al. 2008).Here, we report the creation and functional analysis in planta of new alleles of PINA and PINB. This is the first successful in planta functional analysis of a crop plant protein.     

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.     

Deletion of a Novel F-Box Protein,MUS-10, in Neurospora crassa Leads to Altered Mitochondrial Morphology,Instability of mtDNA and Senescence

While mitochondria are renowned for their role in energy production, they also perform several other integral functions within the cell. Thus, it is not surprising that mitochondrial dysfunction can negatively impact cell viability. Although mitochondria have received an increasing amount of attention in recent years, there is still relatively little information about how proper maintenance of mitochondria and its genomes is achieved. The Neurospora crassa mus-10 mutant was first identified through its increased sensitivity to methyl methanesulfonate (MMS) and was thus believed to be defective in some aspect of DNA repair. Here, we report that mus-10 harbors fragmented mitochondria and that it accumulates deletions in its mitochondrial DNA (mtDNA), suggesting that the mus-10 gene product is involved in mitochondrial maintenance. Interestingly, mus-10 begins to senesce shortly after deletions are visualized in its mtDNA. To uncover the function of MUS-10, we used a gene rescue approach to clone the mus-10 gene and discovered that it encodes a novel F-box protein. We show that MUS-10 interacts with a core component of the Skp, Cullin, F-box containing (SCF) complex, SCON-3, and that its F-box domain is essential for its function in vivo. Thus, we provide evidence that MUS-10 is part of an E3 ubiquitin ligase complex involved in maintaining the integrity of mitochondria and may function to prevent cellular senescence.THE mus-10 mutant was isolated from a screen aimed at identifying Neurospora crassa strains that were sensitive to MMS and therefore likely to lack proper DNA repair mechanisms (Kafer and Perlmutter 1980). Epistasis analyses involving mus-10 suggested that it belonged to the uvs-6 epistasis group, which functions in recombination repair (Kafer and Perlmutter 1980; Kafer 1983). However, mus-10 did not display several phenotypes common to other members of the uvs-6 epistasis group: chromosomal instability, a high sensitivity to histidine, and the inability to produce viable ascospores in homozygous crosses (Newmeyer et al. 1978; Newmeyer and Galeazzi 1978; Kafer and Perlmutter 1980; Kafer 1981; Schroeder 1986; Watanabe et al. 1997; Handa et al. 2000; Sakuraba et al. 2000). Furthermore, the frequencies of spontaneous and radiation-induced mutation observed in mus-10 were similar to those of a wild-type strain (Kafer 1981). Past efforts to uncover the nature of these discrepancies or the function of the mus-10 gene product have been uninformative.The majority of cellular ATP is produced in mitochondria through aerobic respiration, which couples electron flow through respiratory complexes within the mitochondrial inner membrane with oxidative phosphorylation. Besides their role in ATP synthesis, mitochondria are also involved in many other cellular processes including beta-oxidation (Bartlett and Eaton 2004), calcium homeostasis (Gunter et al. 2004; Rimessi et al. 2008), production of iron-sulfur clusters (Zheng et al. 1998; Gerber and Lill 2002; Lill and Muhlenhoff 2005; Rouault and Tong 2005), and apoptosis (Green 2005; Antignani and Youle 2006; Xu and Shi 2007). Although virtually all mitochondrial proteins are encoded within the nucleus, a small number of proteins are encoded by mitochondrial DNA (mtDNA). The integrity of the mitochondrial genome may affect cell survival as mutations in mtDNA accumulate in patients suffering from severe neurological diseases including Alzheimer''s, Huntington''s and Parkinson''s, as well as several types of cancer (Chatterjee et al. 2006; Higuchi 2007; Krishnan et al. 2007; Reeve et al. 2008). The number of mtDNA mutations also increases with age, suggesting a link between mitochondrial dysfunction and ageing (Cortopassi and Arnheim 1990; Corral-Debrinski et al. 1992; Cortopassi et al. 1992; Simonetti et al. 1992; Reeve et al. 2008). Contrary to the single genome in the nucleus, there are several copies of mtDNA in each mitochondrion. Thus, defects in a few mitochondrial genomes do not necessarily lead to mitochondrial dysfunction. Many patients suffering from mitochondrial diseases exhibit heteroplasmy, a phenomenon in which a mixture of wild-type and mutant mtDNAs exist in a single cell. The ratio of wild-type to mutant mtDNAs is critical in determining the penetrance of the genetic defect, where mutant loads >60% are required to cause respiratory chain dysfunction within an individual cell (Boulet et al. 1992; Chomyn et al. 1992; Sciacco et al. 1994).Even though N. crassa strains are generally deemed immortal if they can be subcultured ∼50 times, a wild-type strain was recently reported to senesce after 12,000 hr of growth, implying that this fungus undergoes natural or programmed ageing (Maheshwari and Navaraj 2008; Kothe et al. 2010). However, replicative life span is also influenced by genetic background as certain mutations can cause progressive deterioration of growth, ultimately leading to death. One such example is the nuclear-encoded natural death (nd), which when mutant causes a senescence phenotype correlating with the accumulation of multiple mtDNA deletions (Sheng 1951; Seidel-Rogol et al. 1989). The deletions of mtDNA in nd occurred between two 70- to 701-bp direct repeats, suggesting that the nd gene product regulates recombination, repair, or replication of mtDNA (Bertrand et al. 1993). Another nuclear mutation, senescence (sen), was isolated from N. intermedia and introgressed into N. crassa (Navaraj et al. 2000). Deletions were also observed in the mtDNA of sen mutants, but unlike those occurring in nd were flanked by 6- to 10-bp repeats typically associated with GC-rich palindromic sequences (D''Souza et al. 2005). The nature of the sequences that flanked the mtDNA deletions in these two mutants supported the existence of two distinct systems of mtDNA recombination in N. crassa: a general system of homologous recombination (system I) and a site-specific mechanism (system II), mediated in part by nd and sen, respectively (Bertrand et al. 1993; D''Souza et al. 2005). The nd and sen mutations have been mapped to linkage groups I and V, respectively, but neither gene has been cloned and the precise function of their gene products remains unclear. Two ultraviolet (UV)-sensitive mutants, uvs-4 and uvs-5, are thought to undergo senescence, but unfortunately, these strains have not been studied in great detail (Schroeder 1970; Perkins et al. 1993; Hausner et al. 2006). Premature senescence has also been observed in cytoplasmic mutants of N. crassa including the E35 and ER-3 stopper mutants that harbor large mtDNA deletions, as well as strains that accumulate mitochondrial plasmids capable of inserting into mtDNA through homologous recombination (de Vries et al. 1986; Akins et al. 1989; Myers et al. 1989; Niagro and Mishra 1989; Court et al. 1991; Alves and Videira 1998).While trying to establish the role of MUS-10 in DNA repair, we discovered that the mus-10 mutant exhibited a shortened life span, an abnormal mitochondrial morphology and mtDNA instability. We cloned the mus-10 gene through its ability to complement the MMS sensitivity of the mus-10 mutant and revealed that it encoded a novel F-box protein. This suggested that MUS-10 is part of an Skp, Cullin, F-box containing (SCF) E3 ubiquitin ligase complex that targets proteins for degradation by the 26S proteasome. The data we present in this article offer proof that an SCF complex can regulate both mitochondrial maintenance and cellular senescence.     

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).     

Estimating the Parameters of Selection on Nonsynonymous Mutations in Drosophila pseudoobscura and D. miranda

We present the results of surveys of diversity in sets of >40 X-linked and autosomal loci in samples from natural populations of Drosophila miranda and D. pseudoobscura, together with their sequence divergence from D. affinis. Mean silent site diversity in D. miranda is approximately one-quarter of that in D. pseudoobscura; mean X-linked silent diversity is about three-quarters of that for the autosomes in both species. Estimates of the distribution of selection coefficients against heterozygous, deleterious nonsynonymous mutations from two different methods suggest a wide distribution, with coefficients of variation greater than one, and with the average segregating amino acid mutation being subject to only very weak selection. Only a small fraction of new amino acid mutations behave as effectively neutral, however. A large fraction of amino acid differences between D. pseudoobscura and D. affinis appear to have been fixed by positive natural selection, using three different methods of estimation; estimates between D. miranda and D. affinis are more equivocal. Sources of bias in the estimates, especially those arising from selection on synonymous mutations and from the choice of genes, are discussed and corrections for these applied. Overall, the results show that both purifying selection and positive selection on nonsynonymous mutations are pervasive.SURVEYS of DNA sequence diversity and divergence are shedding light on a number of questions in evolutionary genetics (for recent reviews, see Akey 2009; Sella et al. 2009). Two of the most important questions of this kind concern the distribution of selection coefficients against deleterious mutations affecting protein sequences and the proportion of amino acid sequence differences between related species that have been fixed by positive selection. Several different methods have been proposed for studying each of these questions, using different features of data on polymorphism and divergence at nonsynonymous and silent sites.For example, the parameters of the distribution of selection coefficients against deleterious amino acid mutations have been estimated by contrasting the numbers of nonsynonymous and silent within-species polymorphisms and fixed differences between species (Sawyer and Hartl 1992; Bustamante et al. 2002; Piganeau and Eyre-Walker 2003; Sawyer et al. 2007); by fitting the frequency spectra of nonsynonymous and silent variants to models of selection, mutation, and drift (Akashi 1999; Eyre-Walker et al. 2006; Keightley and Eyre-Walker 2007; Kryukov et al. 2007; Boyko et al. 2008; Eyre-Walker and Keightley 2009); or by comparing levels of nonsynonymous and silent diversities between species with different population sizes (Loewe and Charlesworth 2006; Loewe et al. 2006). The results of these different approaches generally agree in suggesting that there is a wide distribution of selection coefficients against nonsynonymous mutations and that the mean selection coefficient against heterozygous carriers of such mutations is very small. The results imply that a typical individual from a human population carries several hundred weakly deleterious mutations (Eyre-Walker et al. 2006; Kryukov et al. 2007; Boyko et al. 2008); for a typical Drosophila population, with its much higher level of variability, the number is probably an order of magnitude greater (Loewe et al. 2006; Keightley and Eyre-Walker 2007).The presence of this large load of slightly deleterious mutations in human and natural populations, most of which are held at low frequencies by natural selection, has many implications. From the point of view of understanding human genetic disease, it means that we have to face the likelihood that susceptibility to a disease can be influenced by variants at many loci, each with small effects (Kryukov et al. 2007). The pervasive presence of deleterious mutations throughout the genome contributes to inbreeding depression (Charlesworth and Willis 2009) and may mean that the effective population size is reduced by background selection effects, even in regions of the genome with normal levels of genetic recombination (Loewe and Charlesworth 2007). Their presence may contribute so strongly to Hill–Robertson effects (Hill and Robertson 1966; Felsenstein 1974) that they cause severely reduced levels of diversity and adaptation in low-recombination regions of the genome (Charlesworth et al. 2010) and create a selective advantage to maintaining nonzero levels of recombination (Keightley and Otto 2006; Charlesworth et al. 2010). In addition, having an estimate of the distribution of selection coefficients against deleterious nonsynonymous mutations allows their contribution to between-species divergence to be predicted, providing a way of estimating the fraction of fixed nonsynonymous differences caused by positive selection (Loewe et al. 2006; Boyko et al. 2008; Eyre-Walker and Keightley 2009).It is thus important to collect data that shed light on the properties of selection against nonsynonymous mutations in a wide range of systems and also to compare the results from different methods of estimation, since they are subject to different sources of difficulty and biases. In a previous study, we proposed the use of a comparison between two related species with different effective population sizes for this purpose (Loewe and Charlesworth 2006; Loewe et al. 2006), using Drosophila miranda and D. pseudoobscura as material. These are well suited for this type of study, as they are closely related, live together in similar habitats, and yet have very different levels of silent nucleotide diversity, indicating different effective population sizes (N_e). This study was hampered by our inability to compare the same set of loci across the two species and by the small number of loci that could be used. We here present the results of a much larger study of DNA variation at X-linked and autosomal loci for these two species, using D. affinis as a basis for estimating divergence. We compare the results, applying the method of Loewe et al. (2006) with that of Eyre-Walker and Keightley (2009) for estimating the distribution of deleterious selection coefficients and with McDonald–Kreitman test-based methods for estimating the proportion of nonsynonymous differences fixed by positive selection. While broadly confirming the conclusions from earlier studies, we note some possible sources of bias and describe methods for minimizing their effects.