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.     

From QTL to QTN identification in livestock--winning by points rather than knock-out: a review

Many quantitative trait loci (QTL) affecting economic traits in livestock have now been identified. However, the confidence interval (CI) of individual QTL as determined by linkage analysis often spans tens of map units, containing hundreds of genes. Linkage disequilibrium (LD) mapping can reduce the CI to individual map units, but this reduced interval will still contain tens of genes. Methods suitable for model animals to find and validate specific quantitative trait nucleotides (QTN) underlying the QTL cannot be easily applied to livestock species because of their long generation intervals, the cost of maintaining each animal and the difficulty of producing transgenics or 'knock-outs'. Considering these limitations, we review successful approaches for identifying QTN in livestock and outline a schematic strategy for QTN determination and verification. In addition to linkage and LD mapping, the methods include positional cloning, selection of candidate genes, DNA sequencing and statistical analyses. Concordance determination and functional assays are the critical tests for validation of a QTN; we provide a generalized formula for the probability of concordance by chance. Three genes that meet the burden of proof for QTN identification--DGAT1 in cattle, IGF2 in swine and GDF8 in sheep--are discussed in detail. The genetic and economic ramifications of identified QTN and the horizon for selection and introgression are also considered.     

植物数量性状变异的分子基础与QTL克隆研究进展

探讨数量性状变异规律以便对其进行遗传操纵一直是植物遗传学的一个重要领域。DNA分子标记和QTL作图技术的发展以及拟南芥和水稻全基因组测序的完成极大地促进了植物数量性状分子基础的研究。现已克隆了拟南芥ED1、水稻Hdl、玉米Tb1、番茄fw2．2和Brii9-2-5等控制目标数量性状的基因。数量性状表型变异不仅源于多个数量性状基因（QTL）的分离．而且还受到内外环境的修饰。QTL等位基因变异与孟德尔基因变异具有类似的分子基础,即基因表达或蛋白质功能发生改变。通过分析已克隆的植物QTL的变异特征及分子基础,讨论了植物QTL克隆技术策略,并对QTL研究所面临的挑战和应用前景进行了展望。     

Development and mapping of peach candidate genes involved in fruit quality and their transferability and potential use in other Rosaceae species

The goal of the present study was to identify candidate genes (CGs) involved in fruit quality in peach that can be transferred to other Rosaceae species. Two cDNA libraries from fruit of the “Fantasia” peach cultivar, constructed at two stages of development, were used to generate a set of expressed sequence tag sequences. A total of 1,730 peach unigenes were obtained after clustering. Sequences and corresponding annotations were stored in a relational database and are available through a web interface. Fifty-nine CGs involved in fruit growth and development or fruit quality at maturity, focusing on sweetness, acidity, and phenolic compound content, were selected according to their annotation. Fifty-five primer pairs, designed from peach CG sequences and giving PCR products in peach, were tested in strawberry and 36 gave amplified products. Eight CGs were mapped in peach, 14 in strawberry, four in both species and confirmed the pattern of synteny already proposed using comparative mapping. In peach, the CGs are located in three linkage groups (3, 5, 7), and in strawberry they are distributed in all seven Fragaria linkage groups. Colocalization between some of these CGs and quantitative trait loci for fruit quality traits were identified and are awaiting confirmation in further analyses.     

Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates.     

Quantitative trait loci for glucosinolate accumulation in Brassica rapa leaves

Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F(1)s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. Both comparative genomic analyses based on Arabidopsis-Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.     

Development, genetic mapping of candidate gene-based markers and their significant association with the shoot fly resistance quantitative trait loci in sorghum [Sorghum bicolor (L.) Moench]

The associations of candidate genes with quantitative trait loci (QTL) for insect resistance provide primary insight into the molecular mechanisms of resistance. The objectives of the present study were to genetically map the candidate genes and identify their association with shoot fly resistance, and update the genetic map with new markers to locate additional QTL. In this study, 80 candidate gene (CG)-based markers were developed, targeting the seven most important shoot fly resistance genomic regions reported in our previous study. Of the 17 polymorphic CGs, the allelic polymorphisms of seven genes were significantly associated with 18 major QTL for component traits of resistance in multiple QTL mapping (MQM), and two genes in the single-marker analysis. MQM with an updated map revealed 20 new QTL with LOD and R ² (%) values ranging from 2.6 to 15.6 and 5.5 to 34.5?%, respectively. The susceptible parent 296B contributed resistance at 10 QTL. Interestingly, an orthologous insect resistance gene Cysteine protease-Mir1 (XnhsbmSFC34/SBI-10), previously presumed to be a CG based on synteny with maize, was significantly associated with major QTL for all traits (except seedling vigor) explaining 22.1?% of the phenotypic variation for deadhearts%, a direct measure of shoot fly resistance. Similarly, a NBS?CLRR gene (XnhsbmSFCILP2/SBI-10), involved in rice brown planthopper resistance, was associated with deadhearts% and number of eggs per plant. Beta-1,3-glucanase (XnhsbmSFC4/SBI-10), involved in aphid and brown planthopper resistance, was associated with deadhearts% and leaf glossiness. Comparative QTL analysis revealed the existence of common QTL for shoot fly and other important sorghum insect pests such as greenbug, head bug, and midge. Finally, the associated CGs should aid in elucidating the molecular basis of resistance, high-resolution mapping, and map-based cloning of major QTL, besides providing powerful gene tags for marker-assisted selection of shoot fly resistance.     

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.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.