Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits

Identifying the key drivers of common human diseases and associated signaling pathways remains one of the primary objectives in the biomedical and life sciences. In this respect, common inbred strains of mice have played a crucial role, and recent advances in the development of genomics and bioinformatics tools have significantly enhanced their utility for this purpose. These advances have enabled a more holistic, network-oriented view of biological systems that facilitates elucidation of the underlying causes of disease and the best ways to target them. Success in reconstructing gene networks underlying disease traits (or other complex traits like drug response) and identifying the key drivers of these traits now largely rests on integrative approaches that combine data from multiple different sources. Such integrative genomics approaches that take into account genotypic, molecular profiling and clinical data in segregating mouse populations have recently been developed. Key to this integration has been the development and application of sophisticated algorithms to mine the diversity of data.     

Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

Group II introns: structure, folding and splicing mechanism

Group II introns are large autocatalytic RNAs found in organellar genomes of plants and lower eukaryotes, as well as in some bacterial genomes. Interestingly, these ribozymes share characteristic traits with both spliceosomal introns and non-LTR retrotransposons and may have a common evolutionary ancestor. Furthermore, group II intron features such as structure, folding and catalytic mechanism differ considerably from those of other large ribozymes, making group II introns an attractive model system to gain novel insights into RNA biology and biochemistry. This review explores recent advances in the structural and mechanistic characterization of group II intron architecture and self-splicing.     

Complexities in the genetic dissection of quantitative trait loci

The analysis of complex traits, including those involved in many common diseases, has encountered significant difficulties, and, despite major efforts during the past decade, has had little success. Current advances in genomics, however, promise to change this. Recently, Steinmetz et al. used a new technique to produce the first complete quantitative trait locus (QTL) analysis of a complex trait from phenotype to gene to be published entirely in a single report. This marks a significant advance over previous QTL analyses, which took several years. The work exemplifies some of the complexities of QTL mapping and demonstrates a novel method to resolve the underlying genetic architecture of QTLs in yeast. Here we discuss this work in the general context of genetic dissection of complex traits and QTLs.     

关联分析及其在真菌遗传学研究中的应用

近年来,基于连锁不平衡的关联分析被逐渐应用于解析真菌数量性状的遗传基础。本文在介绍关联分析方法的基础上,综述了关联分析在真菌遗传学中的研究进展,并对其未来的应用前景进行了展望。     

The use of molecular genetics in the improvement of agricultural populations

Substantial advances have been made in the genetic improvement of agriculturally important animal and plant populations through artificial selection on quantitative traits. Most of this selection has been on the basis of observable phenotype, without knowledge of the genetic architecture of the selected characteristics. However, continuing molecular genetic analysis of traits in animal and plant populations is leading to a better understanding of quantitative trait genetics. The genes and genetic markers that are being discovered can be used to enhance the genetic improvement of breeding stock through marker-assisted selection.     

小麦根系特征对干旱胁迫的响应

干旱胁迫时, 小麦(Triticum aestivum)根系率先产生应激响应, 同时向地上部发出信号, 诱导地上部发生生理反应, 从而提高植株抗旱能力。根系构型包括平面几何性状和立体几何结构(即拓扑构型), 具有遗传稳定性和可塑性。干旱胁迫影响根系理化特性, 如根源化学信号、根系细胞酶类和根系渗透作用的响应。根系通过调整其解剖学结构和水分吸收动力等来适应干旱胁迫。该文从根系构型、理化特性和解剖学结构3个方面, 系统阐述了小麦根系特征对干旱胁迫的响应, 并探讨了其与干旱胁迫的关系和当前研究中存在的问题, 以期为相关研究提供参考。     

小麦根系特征对干旱胁迫的响应

干旱胁迫时, 小麦(Triticum aestivum)根系率先产生应激响应, 同时向地上部发出信号, 诱导地上部发生生理反应, 从而提高植株抗旱能力。根系构型包括平面几何性状和立体几何结构(即拓扑构型), 具有遗传稳定性和可塑性。干旱胁迫影响根系理化特性, 如根源化学信号、根系细胞酶类和根系渗透作用的响应。根系通过调整其解剖学结构和水分吸收动力等来适应干旱胁迫。该文从根系构型、理化特性和解剖学结构3个方面, 系统阐述了小麦根系特征对干旱胁迫的响应, 并探讨了其与干旱胁迫的关系和当前研究中存在的问题, 以期为相关研究提供参考。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状,随着平衡育种理念的提出和发展,繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展,全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状, 随着平衡育种理念的提出和发展, 繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展, 全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

Genetic linkage map construction and QTL identification of juvenile growth traits in Torreya grandis

Mei, Prunus mume Sieb. et Zucc., is an ornamental plant popular in East Asia and, as an important member of genus Prunus, has played a pivotal role in systematic studies of the Rosaceae. However, the genetic architecture of botanical traits in this species remains elusive. This paper represents the first genome-wide mapping study of quantitative trait loci (QTLs) that affect stem growth and form, leaf morphology and leaf anatomy in an intraspecific cross derived from two different mei cultivars. Genetic mapping based on a high-density linkage map constricted from 120 SSRs and 1,484 SNPs led to the detection of multiple QTLs for each trait, some of which exert pleiotropic effects on correlative traits. Each QTL explains 3-12% of the phenotypic variance. Several leaf size traits were found to share common QTLs, whereas growth-related traits and plant form traits might be controlled by a different set of QTLs. Our findings provide unique insights into the genetic control of tree growth and architecture in mei and help to develop an efficient breeding program for selecting superior mei cultivars.     

哺乳动物合成生物学在生物医学领域的研究进展

虽然合成生物学还处于早期研究阶段,但最近十年,该领域取得了非常显著的研究进展。合成生物学是以工程学思想为基础,通过人工设计、改造基因线路,从而赋予细胞或生物体新的功能,现已广泛应用于各个领域。随着人们对基因线路设计的深入研究,使得合成生物学研究走向临床应用成为可能。本文将围绕哺乳动物合成生物学在疾病治疗方面的研究进展,介绍基因线路的设计思路和方法、不同诱导因子调控的开环式基因线路以及用于疾病诊疗的闭环式基因环路在生物医学领域的应用。最后对合成生物学走向临床治疗的应用前景和挑战进行展望。     

Evolution by phenotype: a biomedical perspective

Genes are widely assumed to play a major role in the epidemiology of complex chronic diseases, yet attempts to characterize the genetic architecture of such traits have been frustrating. Understanding that evolution works by screening phenotypes rather than genotypes can help explain the source of this frustration. Complex traits are usually the result of long-term, often subtle, gene-environment interactions, such that individual life histories may be as important as population histories in predicting and explaining these traits. Recognizing that the problem is not due to technological limitations can help temper expectations and guide the design of future work in biomedical genetics, by allowing us to focus on better approaches where they exist and on those problems most likely to yield a genetic solution. We may even be forced to re-conceive complex biological causation.     

Do Molecular Markers Inform About Pleiotropy?

The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural.     

The relevance of brain evolution for the biomedical sciences

Most biomedical neuroscientists realize the importance of the study of brain evolution to help them understand the differences and similarities between their animal model of choice and the human brains in which they are ultimately interested. Many think of evolution as a linear process, going from simpler brains, as those of rats, to more complex ones, as those of humans. However, in reality, every extant species' brain has undergone as long a period of evolution as has the human brain, and each brain has its own species-specific adaptations. By understanding the variety of existing brain types, we can more accurately reconstruct the brains of common ancestors, and understand which brain traits (of humans as well as other species) are derived and which are ancestral. This understanding also allows us to identify convergently evolved traits, which are crucial in formulating hypotheses about structure-function relationships in the brain. A thorough understanding of the processes and patterns of brain evolution is essential to generalizing findings from 'model species' to humans, which is the backbone of modern biomedical science.     

Aspects of common marmoset basic biology and life history important for biomedical research

While common marmosets are increasingly used as alternative primate models in biomedical research, their life history, specialized behavior and unique physiology are not well known. This paper describes important marmoset attributes that are particularly relevant for biomedical research, including reproduction, neurobiology, immunology, endocrine signaling, obesity and aging, in addition to fetal and postnatal development. While common marmosets exhibit characteristic anthropoid primate traits, they clearly differ from Old World primates and humans in a variety of functions, including reproduction, endocrine signaling and immunology. These differences, however, permit the use of common marmosets in unconventional research strategies targeted on human pathology.