A new approach to quantify the adaptive potential of gene expression variation in gymnosperms

Variation in patterns of gene expression contributes to phenotypic diversity and can ultimately predict adaptive responses. However, in many cases, the consequences of regulatory mutations on patterns of gene expression and ultimately phenotypic differences remain elusive. A standard way to study the genetic architecture of expression variation in model systems has been to map gene expression variation to genetic loci (Fig. 1a). At the same time, in many nonmodel species, especially for long‐lived organisms, controlled crosses are not feasible. If we are to expand our understanding of the role of regulatory mutations on phenotypes, we need to develop new methodologies to study species under ecologically relevant conditions. In this issue of Molecular Ecology, Verta et al. ( 2013 ) present a new approach to analyse gene expression variation and regulatory networks in gymnosperms (Fig. 1b). They capitalized on the fact that gymnosperm seeds contain an energy storage tissue (the megagametophyte) that is directly derived from a single haploid cell (the megaspore). The authors identified over 800 genes for which expression segregated in this maternally inherited haploid tissue. Based on the observed segregation patterns, these genes (Mendelian Expression Traits) are most probably controlled by biallelic variants at a single locus. Most of these genes also belonged to different regulatory networks, except for one large group of 180 genes under the control of a putative trans‐acting factor. In addition, the approach developed here may also help to uncover the effect of rare recessive mutations, which usually remain hidden in a heterozygous state in diploid individuals. The appeal of the work by Verta et al. ( 2013 ) to study gene expression variation is in its simplicity, which circumvents several of the hurdles behind traditional expression quantitative trait locus (eQTL) studies, and could potentially be applied to a large number of species.     

The candidate gene, Clock, localizes to a strong spawning time quantitative trait locus region in rainbow trout

We applied a candidate gene mapping approach to an existing quantitative trait loci (QTL) data set for spawning date in rainbow trout (Oncorynchus mykiss) to ascertain whether these genes could potentially account for any observed QTL effects. Several genes were chosen for their known or suspected roles in reproduction, circadian, or circannual timing, including salmon-type gonadotropin-releasing hormone 3A and 3B (GnRH3A and GnRH3B), Clock, Period1, and arylalkylamine N-acetlytransferase-1 and -2 (AANAT-1 and AANAT-2). Genes were sequenced, and polymorphisms were identified in parents of two rainbow trout mapping families, one of which was used previously to detect spawn timing QTL. Interval mapping was used to identify associations between genetic markers and spawning date effects. Using a genetic map that was updated with 574 genetic markers (775 total), we found evidence for 11 significant or suggestive QTL regions. Most QTL were only localized within one of the parents; however, a strong QTL region was identified in both female and male parents on linkage group RT-8 that explained 20% and 50% of trait variance, respectively. The Clock gene mapped to this region. Period1 mapped to a region in the female parent associated with a marginal effect (P = .056) on spawn timing. Other candidate genes were not associated with significant QTL effects.     

LOD Score Exclusion Analyses for Candidate QTLs using Random Population Samples

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes as putative QTLs using random population samples. Previously, we developed an LOD score exclusion mapping approach for candidate genes for complex diseases. Here, we extend this LOD score approach for exclusion analyses of candidate genes for quantitative traits. Under this approach, specific genetic effects (as reflected by heritability) and inheritance models at candidate QTLs can be analyzed and if an LOD score is < or = -2.0, the locus can be excluded from having a heritability larger than that specified. Simulations show that this approach has high power to exclude a candidate gene from having moderate genetic effects if it is not a QTL and is robust to population admixture. Our exclusion analysis complements association analysis for candidate genes as putative QTLs in random population samples. The approach is applied to test the importance of Vitamin D receptor (VDR) gene as a potential QTL underlying the variation of bone mass, an important determinant of osteoporosis.     

Candidate gene identification approach: progress and challenges

Although it has been widely applied in identification of genes responsible for biomedically, economically, or even evolutionarily important complex and quantitative traits, traditional candidate gene approach is largely limited by its reliance on the priori knowledge about the physiological, biochemical or functional aspects of possible candidates. Such limitation results in a fatal information bottleneck, which has apparently become an obstacle for further applications of traditional candidate gene approach on many occasions. While the identification of candidate genes involved in genetic traits of specific interest remains a challenge, significant progress in this subject has been achieved in the last few years. Several strategies have been developed, or being developed, to break the barrier of information bottleneck. Recently, being a new developing method of candidate gene approach, digital candidate gene approach (DigiCGA) has emerged and been primarily applied to identify potential candidate genes in some studies. This review summarizes the progress, application software, online tools, and challenges related to this approach.     

Genetic Regulation of Bone Metabolism in the Chicken: Similarities and Differences to Mammalian Systems

Birds have a unique bone physiology, due to the demands placed on them through egg production. In particular their medullary bone serves as a source of calcium for eggshell production during lay and undergoes continuous and rapid remodelling. We take advantage of the fact that bone traits have diverged massively during chicken domestication to map the genetic basis of bone metabolism in the chicken. We performed a quantitative trait locus (QTL) and expression QTL (eQTL) mapping study in an advanced intercross based on Red Junglefowl (the wild progenitor of the modern domestic chicken) and White Leghorn chickens. We measured femoral bone traits in 456 chickens by peripheral computerised tomography and femoral gene expression in a subset of 125 females from the cross with microarrays. This resulted in 25 loci for female bone traits, 26 loci for male bone traits and 6318 local eQTL loci. We then overlapped bone and gene expression loci, before checking for an association between gene expression and trait values to identify candidate quantitative trait genes for bone traits. A handful of our candidates have been previously associated with bone traits in mice, but our results also implicate unexpected and largely unknown genes in bone metabolism. In summary, by utilising the unique bone metabolism of an avian species, we have identified a number of candidate genes affecting bone allocation and metabolism. These findings can have ramifications not only for the understanding of bone metabolism genetics in general, but could also be used as a potential model for osteoporosis as well as revealing new aspects of vertebrate bone regulation or features that distinguish avian and mammalian bone.     

Pathway-based analysis using reduced gene subsets in genome-wide association studies

Background  

Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study.     

Pleiotropic effects of the duplicate maize FLORICAULA/LEAFY genes zfl1 and zfl2 on traits under selection during maize domestication

Phenotypic variation on which selection can act during evolution may be caused by variation in activity level of developmental regulatory genes. In many cases, however, such genes affect multiple traits. This situation can lead to co-evolution of traits, or evolutionary constraint if some pleiotropic effects are detrimental. Here, we present an analysis of quantitative traits associated with gene copy number of two important maize regulatory genes, the duplicate FLORICAULA/LEAFY orthologs zfl1 and zfl2. We found statistically significant associations between several quantitative traits and copy number of both zfl genes in several maize genetic backgrounds. Despite overlap in traits associated with these duplicate genes, zfl1 showed stronger associations with flowering time, while zfl2 associated more strongly with branching and inflorescence structure traits, suggesting some divergence of function. Since zfl2 associates with quantitative variation for ear rank and also maps near a quantitative trait locus (QTL) on chromosome 2 controlling ear rank differences between maize and teosinte, we tested whether zfl2 might have been involved in the evolution of this trait using a QTL complementation test. The results suggest that zfl2 activity is important for the QTL effect, supporting zfl2 as a candidate gene for a role in morphological evolution of maize.     

Integrating candidate gene and quantitative genetic approaches to understand variation in timing of breeding in wild tit populations

Two commonly used techniques for estimating the effect of genes on traits in wild populations are the candidate gene approach and quantitative genetic analyses. However, whether these two approaches measure the same underlying processes remains unresolved. Here, we use these two methods to test whether they are alternative or complementary approaches to understanding genetic variation in the timing of reproduction - a key trait involved in adaptation to climate change - in wild tit populations. Our analyses of the candidate gene Clock show weak correlates with timing variables in blue tits, but no association in great tits, confirming earlier results. Quantitative genetic analyses revealed very low levels of both direct (female) and indirect (male) additive genetic variation in timing traits for both species, in contrast to previous studies on these traits, and much lower than generally assumed. Hence, neither method suggests strong genetic effects on the timing of breeding in birds, and further work should seek to assess the generality of these conclusions. We discuss how differences in the genetic control of traits, species life-history and confounding environmental variables may determine how useful integrating these two techniques is to understand the phenotypic variation in wild populations.     

Microtubules in early development of the megagametophyte of <Emphasis Type="Italic">Ginkgo biloba</Emphasis>

Food storage tissue in the seeds of gymnosperms is female gametophyte (megagametophyte) that develops before fertilization, whereas, in seeds of angiosperms, food is stored as endosperm initiated by double fertilization. The megagametophyte is haploid, and endosperm is usually triploid, at least initially. Despite differences in origin, ploidy level, and developmental trigger, the early events of female gametophyte development in ginkgo are very similar to nuclear endosperm development in the seeds of angiosperms. In both, development begins as a single cell that undergoes multiple mitoses without cytokinesis, to produce a large syncytium. This study provided evidence that microtubule involvement in organization of the syncytium into nuclear cytoplasmic domains (NCDs) via nuclear-based radial microtubule systems is a critical developmental feature in the ginkgo megagametophyte, as it is in endosperm. Once the initial anticlinal walls have been deposited at the boundaries of NCDs, cellularization proceeds by the process of alveolation. Continued unidirectional growth of the alveolar walls is an outstanding example of polar cytokinesis. Ginkgo megagametophyte development appears to occur uniformly throughout the entire chamber, whereas nuclear type endosperm usually exhibits distinct developmental domains. These observations suggest that there is a fundamental pathway for the development and cellularization of syncytia in seed development.     

To clone or not to clone plant QTLs: present and future challenges

Recent technical advancements and refinement of analytical methods have enabled the loci (quantitative trait loci, QTLs) responsible for the genetic control of quantitative traits to be dissected molecularly. To date, most plant QTLs have been cloned using a positional cloning approach following identification in experimental crosses. In some cases, an association between sequence variation at a candidate gene and a phenotype has been established by analysing existing genetic accessions. These strategies can be refined using appropriate genetic materials and the latest developments in genomics platforms. We foresee that although QTL analysis and cloning addressing naturally occurring genetic variation should shed light on mechanisms of plant adaptation, a greater emphasis on approaches relying on mutagenesis and candidate gene validation is likely to accelerate the pace of discovering the genes underlying QTLs.     

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF,OMIM and PubMed records

Background

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

Results

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

Conclusion

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.     

利用果蝇模型研究人类心脏早期发育的分子机理(英文)

近年来 ,果蝇心脏特化的遗传机制已初步研究清楚 ,但控制人类心脏早期发育的基因尚待鉴定。因为调控果蝇和脊椎动物早期心脏细胞命运定型的途径具有保守性 ,果蝇是一种探讨人类心脏早期发育的分子机理的理想动物模式。为此目的 ,我们采用P转座子和EMS诱变技术建立了约 3 0 0 0个隐性致死基因平衡系。通过心脏前体细胞特异性抗体免疫组化筛选 ,我们检出 2 0 0余个表现心脏突变表型的平衡致死系。我们进一步利用RNAi技术对一些基因的功能进行了初步的研究 ,证明这些基因表现RNAi的突变表型 ,该类突变表型与基因突变时表现的表型相似 ,即心管呈缺陷型或无心脏前体细胞形成。利用果蝇和人类基因组计划获得的成果 ,我们从果蝇心脏侯选基因中初步克隆和鉴定了 5 0个人类同源基因 ,其中 2 0个是新基因。Northen印迹分析表明 ,一部分人类基因在心脏组织中有表达 ,从而为研究这些基因在人类心脏早期发育中的作用提供了信息。目前 ,我们正在建立转基因果蝇 ,以此为模型研究这些基因是否对心肌细胞发生或心肌功能起调控作用。产生心肌细胞突变类型的基因如果类似于人类心脏病综合症 ,则可以作为人类心脏疾病侯选基因作进一步的分析。     

From QTL to QTN: Candidate Gene Set Approach and a Case Study in Porcine IGF1-FoxO Pathway

Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.     

Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

A functional phylogenomic view of the seed plants

A novel result of the current research is the development and implementation of a unique functional phylogenomic approach that explores the genomic origins of seed plant diversification. We first use 22,833 sets of orthologs from the nuclear genomes of 101 genera across land plants to reconstruct their phylogenetic relationships. One of the more salient results is the resolution of some enigmatic relationships in seed plant phylogeny, such as the placement of Gnetales as sister to the rest of the gymnosperms. In using this novel phylogenomic approach, we were also able to identify overrepresented functional gene ontology categories in genes that provide positive branch support for major nodes prompting new hypotheses for genes associated with the diversification of angiosperms. For example, RNA interference (RNAi) has played a significant role in the divergence of monocots from other angiosperms, which has experimental support in Arabidopsis and rice. This analysis also implied that the second largest subunit of RNA polymerase IV and V (NRPD2) played a prominent role in the divergence of gymnosperms. This hypothesis is supported by the lack of 24nt siRNA in conifers, the maternal control of small RNA in the seeds of flowering plants, and the emergence of double fertilization in angiosperms. Our approach takes advantage of genomic data to define orthologs, reconstruct relationships, and narrow down candidate genes involved in plant evolution within a phylogenomic view of species' diversification.     

A potato molecular-function map for carbohydrate metabolism and transport

Molecular-linkage maps based on functional gene markers (molecular-function maps) are the prerequisite for a candidate-gene approach to identify genes responsible for quantitative traits at the molecular level. Genetic linkage between a quantitative trait locus (QTL) and a candidate-gene locus is observed when there is a causal relationship between alleles of the candidate gene and the QTL effect. Functional gene markers can also be used for marker-assisted selection and as anchors for structural and functional comparisons between distantly related plant species sharing the same metabolic pathways. A first molecular-function map with 85 loci was constructed in potato based on 69 genes. Priority was given to genes operating in carbohydrate metabolism and transport. Public databases were searched for genes of interest from potato, tomato, or other plant species. DNA sequence information was used to develop PCR-based marker assays that allowed the localization of corresponding potato genes on existing RFLP linkage maps. Comparing the molecular-function map for genes operating in carbohydrate metabolism and transport with a QTL map for tuber starch content indicates a number of putative candidate genes for this important agronomic trait. Received: 19 March 2000 / Accepted: 16 May 2000