Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P?相似文献   

Variations in DNA elucidate molecular networks that cause disease

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.     

Identifying Micro RNA and Gene Expression Networks Using Graph Communities

Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies.Although it becomes clear that micro RNAs,one type of epigenetic factors,have direct effect on target genes,it is unclear how micro RNAs perturb downstream genetic neighborhood.Hence,we propose a network community approach to integrate micro RNA and gene expression profiles,to construct an integrative genetic network perturbed by micro RNAs.We apply this approach to an ovarian cancer dataset from The Cancer Genome Atlas project to identify the fluctuation of micro RNA expression and its effects on gene expression.First,we perform expression quantitative loci analysis between micro RNA and gene expression profiles via both a classical regression framework and a sparse learning model.Then,we apply the spin glass community detection algorithm to find genetic neighborhoods of the micro RNAs and their associated genes.Finally,we construct an integrated network between micro RNA and gene expression based on their community structure.Various disease related micro RNAs and genes,particularly related to ovarian cancer,are identified in this network.Such an integrative network allows us to investigate the genetic neighborhood affected by micro RNA expression that may lead to disease manifestation and progression.     

Human metabolic individuality in biomedical and pharmaceutical research

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.     

Advances on methods for mapping QTL in plant

Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.     

Epistasis and balanced polymorphism influencing complex trait variation

Complex traits such as human disease, growth rate, or crop yield are polygenic, or determined by the contributions from numerous genes in a quantitative manner. Although progress has been made in identifying major quantitative trait loci (QTL), experimental constraints have limited our knowledge of small-effect QTL, which may be responsible for a large proportion of trait variation. Here, we identified and dissected a one-centimorgan chromosome interval in Arabidopsis thaliana without regard to its effect on growth rate, and examined the signature of historical sequence polymorphism among Arabidopsis accessions. We found that the interval contained two growth rate QTL within 210 kilobases. Both QTL showed epistasis; that is, their phenotypic effects depended on the genetic background. This amount of complexity in such a small area suggests a highly polygenic architecture of quantitative variation, much more than previously documented. One QTL was limited to a single gene. The gene in question displayed a nucleotide signature indicative of balancing selection, and its phenotypic effects are reversed depending on genetic background. If this region typifies many complex trait loci, then non-neutral epistatic polymorphism may be an important contributor to genetic variation in complex traits.     

A new statistical method for mapping QTLs underlying endosperm traits

Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci （QTLs） underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs controlling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to endosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the precision of QTL mapping. The simulation results show the proposed method provides accurate estimates of the QTL effects and locations with high statistical power.     

A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene

Type 1 diabetes (T1D) in children results from autoimmune destruction of pancreatic beta cells, leading to insufficient production of insulin. A number of genetic determinants of T1D have already been established through candidate gene studies, primarily within the major histocompatibility complex but also within other loci. To identify new genetic factors that increase the risk of T1D, we performed a genome-wide association study in a large paediatric cohort of European descent. In addition to confirming previously identified loci, we found that T1D was significantly associated with variation within a 233-kb linkage disequilibrium block on chromosome 16p13. This region contains KIAA0350, the gene product of which is predicted to be a sugar-binding, C-type lectin. Three common non-coding variants of the gene (rs2903692, rs725613 and rs17673553) in strong linkage disequilibrium reached genome-wide significance for association with T1D. A subsequent transmission disequilibrium test replication study in an independent cohort confirmed the association. These results indicate that KIAA0350 might be involved in the pathogenesis of T1D and demonstrate the utility of the genome-wide association approach in the identification of previously unsuspected genetic determinants of complex traits.     

Statistical approaches in QTL mapping and molecular breeding for complex traits

Most of the important agronomic traits in crops,such as yield and quality,are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction.Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs.Conventionally,the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects.With recent advances in molecular genotyping and high-throughput technology,the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible.The improvement of complex traits has also been achieved by pyramiding individual QTL.In this review,we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction,and discuss their applications in crop breeding for complex traits.     

Molecular and phenotypic variation in the achaete-scute region of Drosophila melanogaster

Variation in quantitative characters underlies much adaptive evolution and provides the basis for selective improvement of domestic species, yet the genetic nature of quantitative variation is poorly understood. Many loci affecting quantitative traits have been identified by the segregation of mutant alleles with major qualitative effects. These alleles may represent an extreme of a continuum of allelic effects, and most quantitative variation could result from the segregation of alleles with subtle effects at loci identified by alleles with major effects. The achaete-scute complex in Drosophila melanogaster plays a central part in bristle development and has been characterized at the molecular level. The hypothesis that naturally occurring quantitative variation in bristle number could be associated with wild-type alleles of achaete-scute was tested by correlating phenotypic variation in bristle number with molecular variation in restriction maps in this region among chromosomes extracted from natural populations. DNA insertion variation in the achaete-scute region was found to be strongly associated with variation in bristle number.     

动态复杂性状遗传结构研究的统计模型

许多重要的农艺性状、生物学和生物医学性状都是数量性状，这些性状在不同的发育阶段发生变化，并表现出复杂的动态特征。针对这些动态性状，传统的遗传作图方法是通过在不同的年龄或发育阶段利用遗传标记与表型性状进行关联分析，并比较这些性状在不同发育阶段的差异，或者通过进行不同阶段的多位点作图进行分析。然而，这些方法并不能确切地反映整个发育过程和动态特点，这使得对性状遗传结构的推测受到限制。要克服这一困难，函数作图将为动态性状的遗传学研究提供一条有效的途径。函数作图综合了生物学机制的数学特性和性状遗传作图的统计学特点，结合统计模型、遗传学和发育生物学的函数作图策略，力求解决诸如发育的遗传控制模式、QTL的持续效应以及引起发育过程中启动和终止的遗传机制等问题。笔者提出的函数作图策略将提供一个研究基因作用及互作与发育模式之间有效的量化检测平台。     

Mapping determinants of human gene expression by regional and genome-wide association

To study the genetic basis of natural variation in gene expression, we previously carried out genome-wide linkage analysis and mapped the determinants of approximately 1,000 expression phenotypes. In the present study, we carried out association analysis with dense sets of single-nucleotide polymorphism (SNP) markers from the International HapMap Project. For 374 phenotypes, the association study was performed with markers only from regions with strong linkage evidence; these regions all mapped close to the expressed gene. For a subset of 27 phenotypes, analysis of genome-wide association was performed with >770,000 markers. The association analysis with markers under the linkage peaks confirmed the linkage results and narrowed the candidate regulatory regions for many phenotypes with strong linkage evidence. The genome-wide association analysis yielded highly significant results that point to the same locations as the genome scans for about 50% of the phenotypes. For one candidate determinant, we carried out functional analyses and confirmed the variation in cis-acting regulatory activity. Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases.     

New gene functions in megakaryopoiesis and platelet formation

Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.