Identifying novel genes for atherosclerosis through mouse-human comparative genetics

Susceptibility to atherosclerosis is determined by both environmental and genetic factors. Its genetic determinants have been studied by use of quantitative-trait-locus (QTL) analysis. So far, 21 atherosclerosis QTLs have been identified in the mouse: 7 in a high-fat-diet model only, 9 in a sensitized model (apolipoprotein E- or LDL [low-density lipoprotein] receptor-deficient mice) only, and 5 in both models, suggesting that different gene sets operate in each model and that a subset operates in both. Among the 27 human atherosclerosis QTLs reported, 17 (63%) are located in regions homologous (concordant) to mouse QTLs, suggesting that these mouse and human atherosclerosis QTLs have the same underlying genes. Therefore, genes regulating human atherosclerosis will be found most efficiently by first finding their orthologs in concordant mouse QTLs. Novel mouse QTL genes will be found most efficiently by using a combination of the following strategies: identifying QTLs in new crosses performed with previously unused parental strains; inducing mutations in large-scale, high-throughput mutagenesis screens; and using new genomic and bioinformatics tools. Once QTL genes are identified in mice, they can be tested in human association studies for their relevance in human atherosclerotic disease.     

Large-scale in silico mapping of complex quantitative traits in inbred mice

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with approximately 40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.     

Integration of genetic and genomic methods for identification of genes and gene variants encoding QTLs in the nonhuman primate

We have developed an integrated approach, using genetic and genomic methods, in conjunction with resources from the Southwest National Primate Research Center (SNPRC) baboon colony, for the identification of genes and their functional variants that encode quantitative trait loci (QTL). In addition, we use comparative genomic methods to overcome the paucity of baboon specific reagents and to augment translation of our findings in a nonhuman primate (NHP) to the human population. We are using the baboon as a model to study the genetics of cardiovascular disease (CVD). A key step for understanding gene–environment interactions in cardiovascular disease is the identification of genes and gene variants that influence CVD phenotypes. We have developed a sequential methodology that takes advantage of the SNPRC pedigreed baboon colony, the annotated human genome, and current genomic and bioinformatic tools. The process of functional polymorphism identification for genes encoding QTLs involves comparison of expression profiles for genes and predicted genes in the genomic region of the QTL for individuals discordant for the phenotypic trait mapping to the QTL. After comparison, genes of interest are prioritized, and functional polymorphisms are identified in candidate genes by genotyping and quantitative trait nucleotide analysis. This approach reduces the time and labor necessary to prioritize and identify genes and their polymorphisms influencing variation in a quantitative trait compared with traditional positional cloning methods.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

玉米抗甘蔗花叶病毒基因的比较定位

收集了玉米抗甘蔗花叶病毒基因/QTL定位信息, 借助玉米遗传图谱IBM2 2005 Neighbors进行了整合。在国内外研究中, 累计报道81个抗病毒基因位点, 分布在玉米7条染色体上, 比较定位发现这些位点集中分布于第3和6染色体。采用元分析技术, 确定3个“一致性”抗病毒QTL, 其中1个位于第3染色体, 在遗传图谱IBM2 2005 Neighbors上覆盖的范围为6.44 cM; 2个位于第6染色体, 覆盖范围分别为6.16 cM和27.48 cM。借助比较基因组学策略, 在第3染色体“一致性”QTL区间内筛选出4个抗病位置候选基因。该研究结果为确定和克隆抗病主效基因提供了基础。     

Differences in DBA/1J and DBA/2J reveal lipid QTL genes

Recent advances in mouse genomics have revealed considerable variation in the form of single-nucleotide polymorphisms (SNPs) among common inbred strains. This has made it possible to characterize closely related strains and to identify genes that differ; such genes may be causal for quantitative phenotypes. The mouse strains DBA/1J and DBA/2J differ by just 5.6% at the SNP level. These strains exhibit differences in a number of metabolic and lipid phenotypes, such as plasma levels of triglycerides (TGs) and HDL. A cross between these strains revealed multiple quantitative trait loci (QTLs) in 294 progeny. We identified significant TG QTLs on chromosomes (Chrs) 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16, and 19, and significant HDL QTLs on Chrs 3, 9, and 16. Some QTLs mapped to chromosomes with limited variability between the two strains, thus facilitating the identification of candidate genes. We suggest that Tshr is the QTL gene for Chr 12 TG and HDL levels and that Ihh may account for the TG QTL on Chr 1. This cross highlights the advantage of crossing closely related strains for subsequent identification of QTL genes.     

From genome to aetiology in a multifactorial disease, type 1 diabetes

The common autoimmune disease type 1 diabetes provides a paradigm for the genetic analysis of multifactorial disease. Disease occurrence is attributable to the interaction with the environment of alleles at many loci interspersed throughout the genome. Their mapping and identification is difficult because the disease-associated alleles occur almost as commonly in patients as in healthy individuals; even the highest-risk genotypes bestow only modest risks of disease. The identification of common quantitative trait loci (QTL) in autoimmune disease and in other common disorders, therefore, requires a very close marriage of genetics and biology. Two QTLs have been identified in human type 1 diabetes: the major histocompatibility complex HLA class II loci and a promoter polymorphism of the insulin gene. The evidence for their primary roles in disease aetiology demonstrates the necessity of combined studies of genetics and biology. Their functions and interaction underpin an emerging picture of the basic causes of the disease and direct analyses towards other candidate genes and pathways. The genetic tools used for QTL identification include transgenesis and gene knockouts, whole genome scanning for linkage, mouse congenic strains, linkage disequilibrium mapping, and the establishment of ancestral haplotypes among disease-associated chromosomes.     

Combined genetic and physiological analysis of a locus contributing to quantitative variation

The natural variation of many traits is controlled by multiple genes, individually referred to as quantitative trait loci (QTL), that interact with the environment to determine the ultimate phenotype of any individual. A QTL has yet to be described molecularly, in part because strategies to systematically identify them are underdeveloped and because the subtle nature of QTLs prevents the application of standard methods of gene identification. Therefore, it will be necessary to develop a systematic approach(es) for the identification of QTLs based upon the numerous positional data now being accumulated through molecular marker analyses. We have characterized a QTL by the following three-step approach: (1) identification of a QTL in complex populations, (2) isolation and genetic mapping of this QTL in near-isogenic lines, and (3) identification of a candidate gene using map position and physiological criteria. Using this approach we have characterized a plant height QTL in maize that maps to chromosome 9 near the centromere. Both map position and physiological criteria suggest the gibberillin biosynthesis gene dwarf3 as a candidate gene for this QTL.     

Network Analysis Identifies ELF3 as a QTL for the Shade Avoidance Response in Arabidopsis

Quantitative Trait Loci (QTL) analyses in immortal populations are a powerful method for exploring the genetic mechanisms that control interactions of organisms with their environment. However, QTL analyses frequently do not culminate in the identification of a causal gene due to the large chromosomal regions often underlying QTLs. A reasonable approach to inform the process of causal gene identification is to incorporate additional genome-wide information, which is becoming increasingly accessible. In this work, we perform QTL analysis of the shade avoidance response in the Bayreuth-0 (Bay-0, CS954) x Shahdara (Sha, CS929) recombinant inbred line population of Arabidopsis. We take advantage of the complex pleiotropic nature of this trait to perform network analysis using co-expression, eQTL and functional classification from publicly available datasets to help us find good candidate genes for our strongest QTL, SAR2. This novel network analysis detected EARLY FLOWERING 3 (ELF3; AT2G25930) as the most likely candidate gene affecting the shade avoidance response in our population. Further genetic and transgenic experiments confirmed ELF3 as the causative gene for SAR2. The Bay-0 and Sha alleles of ELF3 differentially regulate developmental time and circadian clock period length in Arabidopsis, and the extent of this regulation is dependent on the light environment. This is the first time that ELF3 has been implicated in the shade avoidance response and that different natural alleles of this gene are shown to have phenotypic effects. In summary, we show that development of networks to inform candidate gene identification for QTLs is a promising technique that can significantly accelerate the process of QTL cloning.     

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.     

Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era

Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12 284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.     

QTL‐seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker‐assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time‐consuming and labor‐intensive. Here we report the rapid identification of plant QTLs by whole‐genome resequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL‐seq as applied to plant species. We applied QTL‐seq to rice recombinant inbred lines and F₂ populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL‐seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.     

Combined-cross analysis of genome-wide linkage scans for experimental autoimmune encephalomyelitis in rat

Unbiased genetic analysis of experimental autoimmune encephalomyelitis (EAE) can provide insights into the pathogenesis of multiple sclerosis. To date five genome-wide scans using F2 crosses between different inbred rats have been performed with the aim of defining EAE-regulating quantitative trait loci (QTLs) as the starting point for identification of the underlying genes. We here report the first combined-cross analysis of three F2 crosses previously performed in our group. The majority of QTLs was shared between the different strain combinations and was therefore reproduced by the combined-cross analysis. Consequently, combined-cross analysis improved the power to detect QTLs with modest effects and narrowed QTL confidence intervals. The findings also demonstrate a lack of power in previous F2 crosses and encourage future use of larger populations. Moreover, the allelic states of shared QTLs could be established, thus providing critical information for narrowing QTLs and identifying the key polymorphism by subsequent haplotype analysis.     

Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci

We previously identified a Chr 15 quantitative trait locus (QTL) Fob3b in lines of mice selected for high (Fat line) and low (Lean line) body fat content that represent a unique model of polygenic obesity. Here we genetically dissected the Fob3b interval by analyzing the phenotypes of eight overlapping congenic lines and four F₂ congenic intercrosses and prioritized candidates by bioinformatics approaches. Analyses revealed that the Fob3b QTL consists of at least two separate linked QTLs Fob3b1 and Fob3b2. They exhibit additive inheritance and are linked in coupling with alleles originating from the Lean line, decreasing obesity-related traits. In further analyses, we focused on Fob3b1 because it had a larger effect on obesity-related traits than Fob3b2, e.g., the difference between homozygotes for adiposity index (ADI) percentage was 1.22 and 0.77% for Fob3b1 and Fob3b2, respectively. A set of bioinformatics tools was used to narrow down positional candidates from 85 to 4 high-priority Fob3b1 candidates. A previous single Fob3b QTL was therefore resolved into another two closely linked QTLs, confirming the fractal nature of QTLs mapped at low resolution. The interval of the original Fob3b QTL was narrowed from 22.39 to 4.98 Mbp for Fob3b1 and to 7.68 Mbp for Fob3b2, which excluded the previously assigned candidate squalene epoxidase (Sqle) as the causal gene because it maps proximal to refined Fob3b1 and Fob3b2 intervals. A high-resolution map along with prioritization of Fob3b1 candidates by bioinformatics represents an important step forward to final identification of the Chr 15 obesity QTL.     

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.     

Mapping of quantitative trait loci affecting resistance/susceptibility to Sarcocystis miescheriana in swine

The outcome of infectious diseases in vertebrates is under genetic control at least to some extent. In swine, e.g., marked differences in resistance/susceptibility to Sarcocystis miescheriana have been shown between Chinese Meishan and European Pietrain pigs, and these differences are associated with high heritabilities. A first step toward the identification of genes and polymorphisms causal for these differences may be the mapping of quantitative trait loci (QTLs). Considering clinical, immunological, and parasitological traits in the above model system, this survey represents the first QTL study on parasite resistance in pigs. QTL mapping was performed in 139 F(2) pigs of a Meishan/Pietrain family infected with S. miescheriana. Fourteen genome-wide significant QTLs were mapped to several chromosomal areas. Among others, major QTLs were identified for bradyzoite numbers in skeletal muscles (F = 17.4; p < 0.001) and for S. miescheriana-specific plasma IgG(2) levels determined 42 days p.i. (F = 20.9; p < 0.001). The QTLs were mapped to different regions of chromosome 7, i.e., to the region of the major histocompatibility complex (bradyzoites) and to an immunoglobulin heavy chain cluster, respectively. These results provide evidence for a direct and causal role for gene variants within these gene clusters (cis-acting) in differences in resistance to S. miescheriana.     

Identification, isolation and pyramiding of quantitative trait loci for rice breeding

Many agronomically important traits are governed by several genes known as quantitative trait loci (QTLs). The identification of important, QTL-controlled agricultural traits has been difficult because of their complex inheritance; however, completion of the rice genomic sequence has facilitated the cloning of QTLs and their pyramiding for breeding. Because QTLs are derived from natural variation, the use of a wider range of variations such as that found in wild species is important. In addition, Introgression Lines (ILs) developed from wild species in combination with Marker Assisted Selection should facilitate efficient gene identification. This review describes recent developments in rice QTL analysis including mapping, cloning and pyramiding QTLs.     

Quantitative Trait Locus (QTL) meta-analysis and comparative genomics for candidate gene prediction in perennial ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.)

Background

In crop species, QTL analysis is commonly used for identification of factors contributing to variation of agronomically important traits. As an important pasture species, a large number of QTLs have been reported for perennial ryegrass based on analysis of biparental mapping populations. Further characterisation of those QTLs is, however, essential for utilisation in varietal improvement programs.

Results

A bibliographic survey of perennial ryegrass trait-dissection studies identified a total of 560 QTLs from previously published papers, of which 189, 270 and 101 were classified as morphology-, physiology- and resistance/tolerance-related loci, respectively. The collected dataset permitted a subsequent meta-QTL study and implementation of a cross-species candidate gene identification approach. A meta-QTL analysis based on use of the BioMercator software was performed to identify two consensus regions for pathogen resistance traits. Genes that are candidates for causal polymorphism underpinning perennial ryegrass QTLs were identified through in silico comparative mapping using rice databases, and 7 genes were assigned to the p150/112 reference map. Markers linked to the Lp DGL1, Lp Ph1 and Lp PIPK1 genes were located close to plant size, leaf extension time and heading date-related QTLs, respectively, suggesting that these genes may be functionally associated with important agronomic traits in perennial ryegrass.

Conclusions

Functional markers are valuable for QTL meta-analysis and comparative genomics. Enrichment of such genetic markers may permit further detailed characterisation of QTLs. The outcomes of QTL meta-analysis and comparative genomics studies may be useful for accelerated development of novel perennial ryegrass cultivars with desirable traits.

     

大豆倒伏性相关QTL的整合及Overview分析

倒伏性是影响大豆产量的重要因素,发掘与大豆倒伏相关的基因对于培育抗倒伏优良高产大豆品种具有重要意义。目前利用不同群体所构建的遗传图谱已经定位了大量与大豆倒伏性相关的QTLs。本研究在对已报道的QTLs进行物理整合的基础上,选择元分析方法将这些倒伏性相关的QTLs进一步整合,鉴定出位于C2(6号染色体)、F(13号染色体)、L(19号染色体)这3个连锁群上重复次数较多的QTL区间6个。选用基于统计学原理的Overview方法进行优化,获得了这些QTL在各个连锁群上的有效遗传位置,这些QTL的置信区间长度最小可缩至0.2 cM。通过在这些区间内进一步筛选,获得一个稳定性较好的标记Satt277。本研究可为大豆抗倒伏基因发掘及分子标记辅助选择育种提供理论依据。     

Genetic analysis of resistance to septoria tritici blotch in the French winter wheat cultivars Balance and Apache

The ascomycete Mycosphaerella graminicola is the causal agent of septoria tritici blotch (STB), one of the most destructive foliar diseases of bread and durum wheat globally, particularly in temperate humid areas. A screening of the French bread wheat cultivars Apache and Balance with 30 M. graminicola isolates revealed a pattern of resistant responses that suggested the presence of new genes for STB resistance. Quantitative trait loci (QTL) analysis of a doubled haploid (DH) population with five M. graminicola isolates in the seedling stage identified four QTLs on chromosomes 3AS, 1BS, 6DS and 7DS, and occasionally on 7DL. The QTL on chromosome 6DS flanked by SSR markers Xgpw5176 and Xgpw3087 is a novel QTL that now can be designated as Stb18. The QTLs on chromosomes 3AS and 1BS most likely represent Stb6 and Stb11, respectively, and the QTLs on chromosome 7DS are most probably identical with Stb4 and Stb5. However, the QTL identified on chromosome 7DL is expected to be a new Stb gene that still needs further characterization. Multiple isolates were used and show that not all isolates identify all QTLs, which clearly demonstrates the specificity in the M. graminicola–wheat pathosystem. QTL analyses were performed with various disease parameters. The development of asexual fructifications (pycnidia) in the characteristic necrotic blotches of STB, designated as parameter P, identified the maximum number of QTLs. All other parameters identified fewer but not different QTLs. The segregation of multiple QTLs in the Apache/Balance DH population enabled the identification of DH lines with single QTLs and multiple QTL combinations. Analyses of the marker data of these DH lines clearly demonstrated the positive effect of pyramiding QTLs to broaden resistance spectra as well as epistatic and additive interactions between these QTLs. Phenotyping of the Apache/Balance DH population in the field confirmed the presence of the QTLs that were identified in the seedling stage, but Stb18 was inconsistently expressed and might be particularly effective in young plants. In contrast, an additional QTL for STB resistance was identified on chromosome 2DS that is exclusively and consistently expressed in mature plants over locations and time, but it was also strongly related with earliness, tallness as well as resistance to Fusarium head blight. Although to date no Stb gene has been reported on chromosome 2D, the data provide evidence that this QTL is only indirectly related to STB resistance. This study shows that detailed genetic analysis of contemporary commercial bread wheat cultivars can unveil novel Stb genes that can be readily applied in marker-assisted breeding programs.