Genetic complexity and quantitative trait loci mapping of yeast morphological traits

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.     

Mapping quantitative trait loci in noninbred mosquito crosses

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.     

Combinatorial biosynthesis of medicinal plant secondary metabolites

Combinatorial biosynthesis is a new tool in the generation of novel natural products and for the production of rare and expensive natural products. The basic concept is combining metabolic pathways in different organisms on a genetic level. As a consequence heterologous organisms provide precursors from their own primary and secondary metabolism that are metabolised to the desired secondary product due to the expression of foreign genes. In this review we discuss the possibilities and limitations of combining genes from different organisms and the expression of heterologous genes. Major focuses are fundamentals of the genetic work, used expression systems and latest progress in this field. Combinatorial biosynthesis is discussed for important classes of natural products, including alkaloids (vinblastine, vincristine), terpenoids (artemisinin, paclitaxel) and flavonoids. The role and importance of today's used host organisms is critically described, and the latest approaches discussed to give an outlook for future trends and possibilities.     

Quantitative trait locus mapping in natural populations: progress, caveats and future directions

Over the last 15 years quantitative trait locus (QTL) mapping has become a popular method for understanding the genetic basis of continuous variation in a variety of systems. For example, the technique is now an integral tool in medical genetics, livestock production, plant breeding and population genetics of model organisms. Ten years ago, it was suggested that the method could be used to understand continuous variation in natural populations. In this review I: (i) clarify what is meant by natural population in the QTL context, (ii) discuss whether evolutionary biologists have successfully mapped QTL in natural populations, (iii) highlight some of the questions that have been addressed by QTL mapping in natural populations, (iv) describe how QTL mapping can be conducted in unmanipulated natural populations, (v) highlight some of the limitations of QTL mapping and (vi) try to predict some future directions for QTL mapping in natural populations.     

Progress in the dissection and manipulation of plant vitamin E biosynthesis

Plants contain many unique biosynthetic pathways producing a diverse array of natural products that are important for plant function, agriculture, and human nutrition. The tocochromanols define one such class of compounds, comprised of four tocopherols and four tocotrienols that are collectively termed vitamin E. Tocochromanols are synthesized only by plants and other oxygenic, photosynthetic organisms, and the eight individual compounds vary widely in their vitamin E activities. Vitamin E was recognized as an essential component in mammalian diets in the 1920s and the tocochromanol biosynthetic pathway elucidated from radiotracer studies in the mid 1980s. However, it is only recently that genetic and genomics-based approaches in model photosynthetic organisms have allowed the genes and proteins for tocochromanol synthesis to be isolated, setting the stage for targeted manipulation of tocochromanol levels and types in various crops. This article reviews advancements in our molecular and genetic understanding of the tocochromanol biosynthetic pathway in the model photosynthetic organisms Arabidopsis thaliana and Synechocystis sp. PCC6803 and highlights ongoing efforts to use this knowledge to manipulate the levels of this essential nutrient in food crops.     

Mapping quantitative trait loci in multiple populations of Arabidopsis thaliana identifies natural allelic variation for trichome density

The majority of biological traits are genetically complex. Mapping the quantitative trait loci (QTL) that determine these phenotypes is a powerful means for estimating many parameters of the genetic architecture for a trait and potentially identifying the genes responsible for natural variation. Typically, such experiments are conducted in a single mapping population and, therefore, have only the potential to reveal genomic regions that are polymorphic between the progenitors of the population. What remains unclear is how well the QTL identified in any one mapping experiment characterize the genetics that underlie natural variation in traits. Here we provide QTL mapping data for trichome density from four recombinant inbred mapping populations of Arabidopsis thaliana. By aligning the linkage maps for these four populations onto a common physical map, the results from each experiment were directly compared. Seven of the nine QTL identified are population specific while two were mapped in all four populations. Our results show that many lineage-specific alleles that either increase or decrease trichome density persist in natural populations and that most of this genetic variation is additive. More generally, these findings suggest that the use of multiple populations holds great promise for better understanding the genetic architecture of natural variation.     

Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design

The goal of many plant scientists'' research is to explain natural phenotypic variation in terms of simple changes in DNA sequence. Traditionally, linkage mapping has been the most commonly employed method to reach this goal: experimental crosses are made to generate a family with known relatedness, and attempts are made to identify cosegregation of genetic markers and phenotypes within this family. In vertebrate systems, association mapping (also known as linkage disequilibrium mapping) is increasingly being adopted as the mapping method of choice. Association mapping involves searching for genotype-phenotype correlations in unrelated individuals and often is more rapid and cost-effective than traditional linkage mapping. We emphasize here that linkage and association mapping are complementary approaches and are more similar than is often assumed. Unlike in vertebrates, where controlled crosses can be expensive or impossible (e.g., in humans), the plant scientific community can exploit the advantages of both controlled crosses and association mapping to increase statistical power and mapping resolution. While the time and money required for the collection of genotype data were critical considerations in the past, the increasing availability of inexpensive DNA sequencing and genotyping methods should prompt researchers to shift their attention to experimental design. This review provides thoughts on finding the optimal experimental mix of association mapping using unrelated individuals and controlled crosses to identify the genes underlying phenotypic variation.     

Mapping the epistatic network underlying murine reproductive fatpad variation

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F(10) generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F(2) loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.     

Evolutionary ecology and multidisciplinary approaches to prospecting for monooxygenases as biocatalysts

New techniques to explore microbial diversity have led to resurgent interest in prospecting for natural products (bioprospecting or biodiscovery). Although many bioprospecting projects may share little in common at first glance, the vast majority share one particular challenge. Their targets are rare to very rare members of complex natural assemblages. Despite the advances made in bringing new organisms into cultivation and application of culture-independent techniques to isolation of novel genes there remain systematic biases against relatively rare organisms with specific growth requirements. These can frequently be overcome by application of multidisciplinary approaches that take into account principles of evolutionary ecology. Our experiences with prospecting for soluble di-iron monooxygenases (SDIMO) indicate that conventional approaches to organism isolation and metagenomic cloning systematically under-sample diversity in this enzyme family. This reflects that SDIMO-containing organisms are typically relatively low-abundance members of natural assemblages (thus biased against by direct cloning) and SDIMOs have discrete physiological roles in each organism (thus are not amenable to generic enrichment culture strategies). We have sought to overcome this by a PCR-based survey of gene diversity to guide evaluation of subsequent culture or cloning studies. A surprising outcome of this survey was that conventional PCR approaches using degenerate primers also systematically under-sampled diversity, but nested PCR strategies revealed unprecedented diversity. We conclude that many PCR-based gene-prospecting studies are likely to have under-estimated the impact of target:competitor ratios on their success.     

A genome scan for quantitative trait loci in a wild population of red deer (Cervus elaphus)

Recent empirical evidence indicates that although fitness and fitness components tend to have low heritability in natural populations, they may nonetheless have relatively large components of additive genetic variance. The molecular basis of additive genetic variation has been investigated in model organisms but never in the wild. In this article we describe an attempt to map quantitative trait loci (QTL) for birth weight (a trait positively associated with overall fitness) in an unmanipulated, wild population of red deer (Cervus elaphus). Two approaches were used: interval mapping by linear regression within half-sib families and a variance components analysis of a six-generation pedigree of >350 animals. Evidence for segregating QTL was found on three linkage groups, one of which was significant at the genome-wide suggestive linkage threshold. To our knowledge this is the first time that a QTL for any trait has been mapped in a wild mammal population. It is hoped that this study will stimulate further investigations of the genetic architecture of fitness traits in the wild.     

Model choice in gene mapping: what and why

The choice of an appropriate genetic model describing the genetic architecture underlying a character of interest is an inherent part of the gene mapping studies of human and other living organisms. The genetic model specifies the statistical parameters for the number of genes, their positions, and the types and magnitudes of their contributions to the phenotype. There are many considerations involved in model formulation (choice) ranging from the assumptions concerning the data, the role of environment, and the number of oligogenes (or quantitative trait loci) influencing the trait behavior. There are several model selection procedures and criteria under specific sampling designs in the genetic literature. These approaches often have their origin in computer science or in general statistical theory. Our aim here is to give an overview of the most popular statistical criteria and to present principles behind them. Bayesian model averaging is suggested as a robust alternative for such methods.     

Rapid genetic mapping in Neurospora crassa

Forward genetic analysis is the most broadly applicable approach to discern gene functions. However, for some organisms like the filamentous ascomycete Neurospora crassa, genetic mapping frequently represents a limiting step in forward genetic approaches. We describe an efficient method for genetic mapping in N. crassa that makes use of a modified bulked segregant analysis and PCR-based molecular markers. This method enables mapping with progeny from a single cross and requires only 90 PCR amplifications. Genetic distances between syntenic markers have been determined to ensure complete coverage of the genome and to allow interpolation of linkage data. As a result, most mutations should be mapped in less than one month to within 1-5 map units, a level of resolution sufficient to initiate map-based cloning efforts. This system also will facilitate analyses of recombination at a genome-wide level and is applicable to other perfect fungi when suitable markers are available.     

Synthetic biology: lessons from engineering yeast MAPK signalling pathways

All living cells respond to external stimuli and execute specific physiological responses through signal transduction pathways. Understanding the mechanisms controlling signalling pathways is important for diagnosing and treating diseases and for reprogramming cells with desired functions. Although many of the signalling components in the budding yeast Saccharomyces cerevisiae have been identified by genetic studies, many features concerning the dynamic control of pathway activity, cross‐talk, cell‐to‐cell variability or robustness against perturbation are still incompletely understood. Comparing the behaviour of engineered and natural signalling pathways offers insight complementary to that achievable with standard genetic and molecular studies. Here, we review studies that aim at a deeper understanding of signalling design principles and generation of novel signalling properties by engineering the yeast mitogen‐activated protein kinase (MAPK) pathways. The underlying approaches can be applied to other organisms including mammalian cells and offer opportunities for building synthetic pathways and functionalities useful in medicine and biotechnology.     

高通量测序技术结合正向遗传学手段在基因定位研究中的应用

传统的利用正向遗传学方法的基因定位一般是通过构建遗传连锁图谱进行的,该过程步骤繁琐、耗时耗力,很多情形下定位精确度低、区间大。随着高通量测序技术的快速发展以及测序成本的不断降低,多种简单快捷的利用测序手段定位基因的方法被开发出来,包括对突变体基因组直接测序定位、突变体材料构建混池测序定位和遗传分离群体测序构建图谱定位等,还可以对转录组和部分基因组进行测序定位。这些方法可以在核苷酸水平鉴定突变位点,并已推广到复杂的遗传背景中。近期报道的一些测序定位甚至是在不依赖于参考基因组序列、遗传杂交和连锁信息的情况下完成的,这使得很多非模式物种也能开展正向遗传学研究。本文就这些新技术及其在基因定位中的应用进行了综述。     

Efficient control of population structure in model organism association mapping

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.     

Entering the second century of maize quantitative genetics

Maize is the most widely grown cereal in the world. In addition to its role in global agriculture, it has also long served as a model organism for genetic research. Maize stands at a genetic crossroads, as it has access to all the tools available for plant genetics but exhibits a genetic architecture more similar to other outcrossing organisms than to self-pollinating crops and model plants. In this review, we summarize recent advances in maize genetics, including the development of powerful populations for genetic mapping and genome-wide association studies (GWAS), and the insights these studies yield on the mechanisms underlying complex maize traits. Most maize traits are controlled by a large number of genes, and linkage analysis of several traits implicates a ‘common gene, rare allele'' model of genetic variation where some genes have many individually rare alleles contributing. Most natural alleles exhibit small effect sizes with little-to-no detectable pleiotropy or epistasis. Additionally, many of these genes are locked away in low-recombination regions that encourage the formation of multi-gene blocks that may underlie maize''s strong heterotic effect. Domestication left strong marks on the maize genome, and some of the differences in trait architectures may be due to different selective pressures over time. Overall, maize''s advantages as a model system make it highly desirable for studying the genetics of outcrossing species, and results from it can provide insight into other such species, including humans.     

Trait variation in yeast is defined by population history

A fundamental goal in biology is to achieve a mechanistic understanding of how and to what extent ecological variation imposes selection for distinct traits and favors the fixation of specific genetic variants. Key to such an understanding is the detailed mapping of the natural genomic and phenomic space and a bridging of the gap that separates these worlds. Here we chart a high-resolution map of natural trait variation in one of the most important genetic model organisms, the budding yeast Saccharomyces cerevisiae, and its closest wild relatives and trace the genetic basis and timing of major phenotype changing events in its recent history. We show that natural trait variation in S. cerevisiae exceeds that of its relatives, despite limited genetic variation, and follows the population history rather than the source environment. In particular, the West African population is phenotypically unique, with an extreme abundance of low-performance alleles, notably a premature translational termination signal in GAL3 that cause inability to utilize galactose. Our observations suggest that many S. cerevisiae traits may be the consequence of genetic drift rather than selection, in line with the assumption that natural yeast lineages are remnants of recent population bottlenecks. Disconcertingly, the universal type strain S288C was found to be highly atypical, highlighting the danger of extrapolating gene-trait connections obtained in mosaic, lab-domesticated lineages to the species as a whole. Overall, this study represents a step towards an in-depth understanding of the causal relationship between co-variation in ecology, selection pressure, natural traits, molecular mechanism, and alleles in a key model organism.     

Cultivating the uncultivated: a community genomics perspective

Current isolation methods access only a small subset of the total microbial diversity. Although an isolate traditionally has been required for genomic characterization, the advent of sequencing of entire natural microbial communities enables culture-independent genomic analysis. Information about the genetic potential of uncultivated organisms can be used to predict the form of metabolic interdependencies and nutritional requirements. We believe that this could provide the information necessary to bypass bottlenecks that have inhibited cultivation of many microorganisms. However, it might not be practical or possible to isolate all of the vast number of microbial species and strains for laboratory-based characterization. Ultimately, cultivation-independent genomic and genomically enabled approaches could provide a way to directly analyze microbial activity in its geochemical and ecological context.