High-definition genome profiling for genetic marker discovery

Genetic mapping is a key step towards isolating genes and genetic markers associated with phenotypic traits by elucidating their genetic positions. The success of this approach depends on precision in pinpointing genetic positions and the effectiveness of the discovery process. Recent advances in microarray technology and the increasing availability of genomic information have provided an opportunity to use microarrays to scan effectively for genetic variations at the whole-genome scale, enabling the production of high-definition gene-based genetic maps, in combination with functional analyses and identification of trait-associated genetic marker candidates with high precision. In this review, we discuss the concept, process, tools and applications of microarray-based high-definition genetic analysis. This post-genomics approach should help to identify causative genetic variation by uniting genetic and functional information.     

Advanced technologies for genomic analysis in farm animals and its application for QTL mapping

Rapid progress in farm animal breeding has been made in the last few decades. Advanced technologies for genomic analysis in molecular genetics have led to the identification of genes or markers associated with genes that affect economic traits. Molecular markers, large-insert libraries and RH panels have been used to build the genetic linkage maps, physical maps and comparative maps in different farm animals. Moreover, EST sequencing, genome sequencing and SNPs maps are helping us to understand how genomes function in various organisms and further areas will be studied by DNA microarray technologies and proteomics methods. Because most economically important traits in farm animals are controlled by multiple genes and the environment, the main goal of genome research in farm animals is to map and characterize genes determining QTL. There are two main strategies to identify trait loci, candidate gene association tests and genome scan approaches. In recent years, some new concepts, such as RNAi, miRNA and eQTL, have been introduced into farm animal research, especially for QTL mapping and finding QTN. Several genes that influence important traits have already been identified or are close to being identified, and some of them have been applied in farm animal breeding programs by marker-assisted selection.     

Determining causality and consequence of expression quantitative trait loci

Expression quantitative trait loci (eQTLs) are currently the most abundant and systematically-surveyed class of functional consequence for genetic variation. Recent genetic studies of gene expression have identified thousands of eQTLs in diverse tissue types for the majority of human genes. Application of this large eQTL catalog provides an important resource for understanding the molecular basis of common genetic diseases. However, only now has both the availability of individuals with full genomes and corresponding advances in functional genomics provided the opportunity to dissect eQTLs to identify causal regulatory variants. Resolving the properties of such causal regulatory variants is improving understanding of the molecular mechanisms that influence traits and guiding the development of new genome-scale approaches to variant interpretation. In this review, we provide an overview of current computational and experimental methods for identifying causal regulatory variants and predicting their phenotypic consequences.     

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.     

Will family studies return to prominence in human genetics and genomics? Rare variants and linkage analysis of complex traits

A major focus of modern human genetics has been the search for genetic variations that contribute to human disease. These studies originated in families and used linkage methods as a primary analytical tool. With continued technical improvements, these family-based linkage studies have been very powerful in identifying genes contributing to monogenic disorders. When these methods were applied to disorders with complex, non-Mendelian patterns of inheritance they largely failed. The development of effective capabilities for Genome Wide Association Studies (GWAS) relegated family-based studies to a peripheral role in human genetics research. Despite the remarkable record of GWAS discoveries, common variations identified in GWAS account for a limited (frequently less than 10%) proportion of the heritable risk of qualitative traits or variance of quantitative traits. Next generation sequencing is facilitating a re-examination of family-based methods with surprising and intriguing results. We propose that rare variants of large effect underlie many linkage peaks, including complex quantitative phenotypes, and review the issues underlying this proposed basis for complex traits.     

冠心病易感基因的筛选

作为一种多基因疾病 ,冠心病是由遗传和环境因素共同作用的结果 ,在许多国家是主要的死因之一。由于目前冠心病的发病机制尚不十分清楚 ,阻碍了其易感基因的定位分离研究。冠心病遗传因素的确定 ,显然将有助于其易感基因定位分离研究。迄今除发现了个别的相关基因外 ,绝大部分的遗传易感性相关基因尚未被发现 ,其研究仍然存在许多问题。为此 ,本文就其易感基因可能的研究策略和方法作一综述。这些方法同样也适用于诸如中风、外周血管阻塞、高血压、心力衰竭等心血管疾病以及其它多基因疾病     

遗传与基因表达数据的整合—— eQTL的方法及应用

高通量的基因型分析和芯片技术的发展使人们能够进一步研究哪些遗传差异最终影响基因的表达。通过表达数量性状座位(eQTL)作图方法可对基因表达水平的遗传基础进行解析。与传统的QTL分析方法一样, eQTL的主要目标是鉴别表达性状座位所在的染色体区域。但由于表达谱数据成千上万, 而传统的QTL分析方法最多分析几十个性状, 因此需要考虑这类实验设计的特点以及统计分析方法。本文详细介绍了eQTL定位过程及其研究方法, 重点从个体选择、基因芯片实验设计、基因表达数据的获得与标准化、作图方法及结果分析等方面进行了综述, 指出了当前eQTL研究存在的问题和局限性。最后介绍了eQTL研究在估计基因表达遗传率、挖掘候选基因、构建基因调控网络、理解基因间及基因与环境的互作的应用进展。     

Pathways to the analysis of microarray data

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.     

Novel integrative genomics strategies to identify genes for complex traits

Forward genetics is a common approach to dissecting complex traits like common human diseases. The ultimate aim of this approach was the identification of genes that are causal for disease or other phenotypes of interest. However, the forward genetics approach is by definition restricted to the identification of genes that have incurred mutations over the course of evolution or that incurred mutations as a result of chemical mutagenesis, and that as a result lead to disease or to variations in other phenotypes of interest. Genes that harbour no such mutations, but that play key roles in parts of the biological network that lead to disease, are systematically missed by this class of approaches. Recently, a class of novel integrative genomics approaches has been devised to elucidate the complexity of common human diseases by intersecting genotypic, molecular profiling, and clinical data in segregating populations. These novel approaches take a more holistic view of biological systems and leverage the vast network of gene–gene interactions, in combination with DNA variation data, to establish causal relationships among molecular profiling traits and Fbetween molecular profiling and disease (or other classic phenotypes). A number of novel genes for disease phenotypes have been identified as a result of these approaches, highlighting the utility of integrating orthogonal sources of data to get at the underlying causes of disease.     

Utilization of gene mapping information in livestock animals

A number of recent advances in genomic research will change and improve livestock production in the near future. Genetic linkage maps have been developed for a number of livestock species including cattle, sheep, and pigs. These maps allow scientists to identify chromosomal regions that influence traits of economic importance. This information will lead to improved genetic selection practices by identifying animals with superior copies of the chromosomal regions that affect the selected trait. This mapping information will also be used to identify the genes controlling the trait. A number of genomic regions or loci have already been reported that affect production, carcass or disease traits, and in a few cases, a specific gene has been identified. Production of transgenic animals with sequence changes in these genes may be beneficial for evaluating the effect of the gene upon the selected trait and more specifically the effect of certain polymorphisms (mutations) within the gene.     

Taking stock of complex trait genetics in mice

The mapping of complex trait loci in mice has recently become very popular thanksto dense genetic maps, better approaches to linkage analysis and the continued value of the mouse as a key model organism for human disease. Neverthelless, the ultimate goal remains very difficult: to identify genes that underlie complex traits and to understand their function at a molecular level. In assessing the prospects of current efforts, it helps to review the findings of earlier studies of complex traits and, despite all the technology, to be reminded of the inherent benefits and limitations at the source of genetic variation: the laboratory mouse. With the right perspective it should be possible for geneticists analysing complex triats to take full advantage of the resources that the genome project will provide.     

Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses

Many complex human diseases and traits of biological and/or economic importance are determined by interacting networks of multiple quantitative trait loci (QTL) and environmental factors. Mapping QTL is critical for understanding the genetic basis of complex traits, and for ultimate identification of genes responsible. A variety of sophisticated statistical methods for QTL mapping have been developed. Among these developments, the evolution of Bayesian approaches for multiple QTL mapping over the past decade has been remarkable. Bayesian methods can jointly infer the number of QTL, their genomic positions and their genetic effects. Here, we review recently developed and still developing Bayesian methods and associated computer software for mapping multiple QTL in experimental crosses. We compare and contrast these methods to clearly describe the relationships among different Bayesian methods. We conclude this review by highlighting some areas of future research.     

Genetic basis of yield as viewed from a crop physiologist's perspective

The final yield of a crop is the product of growth during the growing season and a number of developmental processes occurring throughout the life cycle of a crop, with most genes influencing the final outcome to a degree. However, recent advances in molecular biology have developed the potential to identify and map many genes or QTLs related to various important traits, including yield, plant adaptation and tolerance to stresses. Significant G×E interactions for yield have been identified, as have interactions associated with QTLs for yield. However, there is little evidence available to confirm that a QTL for yield from a parental line in one mapping population may improve yield when transferred into an adapted, high‐yielding line of another population. In order to narrow the apparent gap between the genotype and the phenotype with regard to yield, it is important to identify key traits related to yield and then attempt to identify and locate the genes controlling them. The partitioning of the developmental time to anthesis into different phases: from sowing to the onset of stem elongation and from then to anthesis, as a relatively simple physiological attribute putatively related to yield, is discussed. If the relationship holds in a wider range of conditions and the genetic factors responsible are located then the genetic basis of yield should be identified. There has also been significant progress in crop simulation modelling. Using knowledge of crop physiology and empirical relationships these models can simulate the performance of crops, including the G×E interactions. Such models require information regarding the genetic basis of yield, which are included in the form of genetic coefficients. Essentially models are constructed as decision‐making tools for management but may be of use in detecting prospective traits for selection within a breeding programme. Problems associated with this approach are discussed. This review discusses the need to use crop physiology approaches to analyse components of yield in order to reliably identify the genetic basis of yield.     

Gene expression profiling and its practice in drug development

The availability of sequenced genomes of human and many experimental animals necessitated the development of new technologies and powerful computational tools that are capable of exploiting these genomic data and ask intriguing questions about complex nature of biological processes. This gave impetus for developing whole genome approaches that can produce functional information of genes in the form of expression profiles and unscramble the relationships between variation in gene expression and the resulting physiological outcome. These profiles represent genetic fingerprints or catalogue of genes that characterize the cell or tissue being studied and provide a basis from which to begin an investigation of the underlying biology. Among the most powerful and versatile tools are high-density DNA microarrays to analyze the expression patterns of large numbers of genes across different tissues or within the same tissue under a variety of experimental conditions or even between species. The wide spread use of microarray technologies is generating large sets of data that is stimulating the development of better analytical tools so that functions can be predicted for novel genes. In this review, the authors discuss how these profiles are being used at various stages of the drug discovery process and help in the identification of new drug targets, predict the function of novel genes, and understand individual variability in response to drugs.