Identification of diabetes susceptibility loci in db mice by combined quantitative trait loci analysis and haplotype mapping

To identify the disease-susceptibility genes of type 2 diabetes, we performed quantitative trait loci (QTL) analysis in F(2) populations generated from a BKS.Cg-m+/+Lepr(db) and C3H/HeJ intercross, taking advantage of genetically determined obesity and diabetes traits associated with the db gene. A genome-wide scan in the F(2) populations divided by sex and db genotypes identified 14 QTLs in total and 3 major QTLs on chromosome (Chr) 3 (LOD 5.78) for fat pad weight, Chr 15 (LOD 6.64) for body weight, and Chr 16 (LOD 8.15) for blood glucose concentrations. A linear-model-based genome scan using interactive covariates allowed us to consider sex- or sex-by db-specific effects of each locus. For the most significant QTL on Chr 16, the high-resolution haplotype comparison between BKS and C3H strains reduced the critical QTL interval from 20 to 4.6 Mb by excluding shared haplotype regions and identified 11 nonsynonymous single-nucleotide polymorphisms in six candidate genes.     

A large QTL for fear and anxiety mapped using an F2 cross can be dissected into multiple smaller QTLs

Using chromosome substitution strains (CSS), we previously identified a large quantitative trait locus (QTL) for conditioned fear (CF) on mouse chromosome 10. Here, we used an F₂ cross between CSS‐10 and C57BL/6J (B6) to localize that QTL to distal chromosome 10. That QTL accounted for all the difference between CSS‐10 and B6. We then produced congenic strains to fine‐map that interval. We identified two congenic strains that captured some or all the QTL. The larger congenic strain (Line 1: 122.387121–129.068 Mb; build 37) appeared to account for all the difference between CSS‐10 and B6. The smaller congenic strain (Line 2: 127.277–129.068 Mb) was intermediate between CSS‐10 and B6. We used haplotype mapping followed by quantitative polymerase chain reaction to identify one gene that was differentially expressed in both lines relative to B6 (Rnf41) and one that was differentially expressed between only Line 1 and B6 (Shmt2). These cis‐eQTLs may cause the behavioral QTLs; however, further studies are required to validate these candidate genes. More generally, our observation that a large QTL mapped using CSS and F₂ crosses can be dissected into multiple smaller QTLs shows a weaknesses of two‐stage approaches that seek to use coarse mapping to identify large regions followed by fine‐mapping. Indeed, additional dissection of these congenic strains might result in further subdivision of these QTL regions. Despite these limitations, we have successfully fine‐mapped two QTLs to small regions and identified putative candidate genes, showing that the congenic approach can be effective for fine‐mapping QTLs .     

Towards the cloning of genes underlying murine QTLs

Many quantitative trait loci (QTLs), including those for ethanol-related traits, have been mapped in the mouse. In light of rapidly developing tools and resources, we briefly review the strategy for identifying the genes underlying these QTLs. We note that positional cloning will soon be a matter of testing candidate genes rather than discovering genes; therefore, we describe a ``congenic test' to support that a candidate gene is indeed a QTL. Considering the rapid development of congenics and mutants, we also identify four areas of investigation—phenotypes, ethanol specificity, environment, and gene interactions—that might be exploited during the course of positional cloning to gain insights into QTL pathways. In particular, we note that multiple mutants of nearly every major neurotransmitter pathway have now been made. These mutants are not only useful for phenotypic tests, but also could be used to conduct ``gene dependence' tests of QTLs. We also consider potential applications for the very recently developed ability to clone mice. Received: 15 September 1998 / Accepted: 8 October 1998     

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.     

Novel strategies to mine alcoholism-related haplotypes and genes by combining existing knowledge framework

High-throughout single nucleotide polymorphism detection technology and the existing knowledge provide strong support for mining the disease-related haplotypes and genes. In this study, first, we apply four kinds of haplotype identification methods (Confidence Intervals, Four Gamete Tests, Solid Spine of LD and fusing method of haplotype block) into high-throughout SNP genotype data to identify blocks, then use cluster analysis to verify the effectiveness of the four methods, and select the alcoholism-related SNP haplotypes through risk analysis. Second, we establish a mapping from haplotypes to alcoholism-related genes. Third, we inquire NCBI SNP and gene databases to locate the blocks and identify the candidate genes. In the end, we make gene function annotation by KEGG, Biocarta, and GO database. We find 159 haplotype blocks, which relate to the alcoholism most possibly on chromosome 1∼22, including 227 haplotypes, of which 102 SNP haplotypes may increase the risk of alcoholism. We get 121 alcoholism-related genes and verify their reliability by the functional annotation of biology. In a word, we not only can handle the SNP data easily, but also can locate the disease-related genes precisely by combining our novel strategies of mining alcoholism-related haplotypes and genes with existing knowledge framework. Supported by the National Natural Science Foundation of China (Grant Nos. 30570424, 60601010 and 30600367), the National High-Tech Research and Development Program of China, (Grant No.2007AA02Z329), the Key Science and Technology Program of Heilongjiang Province(Grant No.GB03C602-4), Natural Science Foundation of Heilongjiang Province (Grant No. F2008-02), Youth Science Foundation of Harbin Medical University (Grant No. 060045) and Science Foundation of Heilongjiang Province Education Department (Grant Nos. 11531113 and 1152hq28).     

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.     

Fine Mapping and Candidate Gene Search of Quantitative Trait Loci for Growth and Obesity Using Mouse Intersubspecific Subcongenic Intercrosses and Exome Sequencing

Although growth and body composition traits are quantitative traits of medical and agricultural importance, the genetic and molecular basis of those traits remains elusive. Our previous genome-wide quantitative trait locus (QTL) analyses in an intersubspecific backcross population between C57BL/6JJcl (B6) and wild Mus musculus castaneus mice revealed a major growth QTL (named Pbwg1) on a proximal region of mouse chromosome 2. Using the B6.Cg-Pbwg1 intersubspecific congenic strain created, we revealed 12 closely linked QTLs for body weight and body composition traits on an approximately 44.1-Mb wild-derived congenic region. In this study, we narrowed down genomic regions harboring three (Pbwg1.12, Pbwg1.3 and Pbwg1.5) of the 12 linked QTLs and searched for possible candidate genes for the QTLs. By phenotypic analyses of F₂ intercross populations between B6 and each of four B6.Cg-Pbwg1 subcongenic strains with overlapping and non-overlapping introgressed regions, we physically defined Pbwg1.12 affecting body weight to a 3.8-Mb interval (61.5–65.3 Mb) on chromosome 2. We fine-mapped Pbwg1.3 for body length to an 8.0-Mb interval (57.3–65.3) and Pbwg1.5 for abdominal white fat weight to a 2.1-Mb interval (59.4–61.5). The wild-derived allele at Pbwg1.12 and Pbwg1.3 uniquely increased body weight and length despite the fact that the wild mouse has a smaller body size than that of B6, whereas it decreased fat weight at Pbwg1.5. Exome sequencing and candidate gene prioritization suggested that Gcg and Grb14 are putative candidate genes for Pbwg1.12 and that Ly75 and Itgb6 are putative candidate genes for Pbwg1.5. These genes had nonsynonymous SNPs, but the SNPs were predicted to be not harmful to protein functions. These results provide information helpful to identify wild-derived quantitative trait genes causing enhanced growth and resistance to obesity.     

Susceptibility to neoplastic and non-neoplastic pulmonary diseases in mice: genetic similarities

Chronic inflammation predisposes toward many types of cancer. Chronic bronchitis and asthma, for example, heighten the risk of lung cancer. Exactly which inflammatory mediators (e.g., oxidant species and growth factors) and lung wound repair processes (e.g., proangiogenic factors) enhance pulmonary neoplastic development is not clear. One approach to uncover the most relevant biochemical and physiological pathways is to identify genes underlying susceptibilities to inflammation and to cancer development at the same anatomic site. Mice develop lung adenocarcinomas similar in histology, molecular characteristics, and histogenesis to this most common human lung cancer subtype. Over two dozen loci, called Pas or pulmonary adenoma susceptibility, Par or pulmonary adenoma resistance, and Sluc or susceptibility to lung cancer genes, regulate differential lung tumor susceptibility among inbred mouse strains as assigned by QTL (quantitative trait locus) mapping. Chromosomal sites that determine responsiveness to proinflammatory pneumotoxicants such as ozone (O3), particulates, and hyperoxia have also been mapped in mice. For example, susceptibility QTLs have been identified on chromosomes 17 and 11 for O3-induced inflammation (Inf1, Inf2), O3-induced acute lung injury (Aliq3, Aliq1), and sulfate-associated particulates. Sites within the human and mouse genomes for asthma and COPD phenotypes have also been delineated. It is of great interest that several susceptibility loci for mouse lung neoplasia also contain susceptibility genes for toxicant-induced lung injury and inflammation and are homologous to several human asthma loci. These QTLs are described herein, candidate genes are suggested within these sites, and experimental evidence that inflammation enhances lung tumor development is provided.     

Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs

Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.     

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.     

High-throughput sequence identification of gene coding variants within alcohol-related QTLs

Low initial response to alcohol has been shown to be among the best predictors of development of alcoholism. A similar phenotypic measure, difference in initial sensitivity to ethanol, has been used for the genetic selection of two mouse strains, the Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS) mice, and for the subsequent identification of four quantitative trait loci (QTLs) for alcohol sensitivity. We now report the application of high throughput comparative gene sequencing in the search for genes underlying these four QTLs. To carry out this search, over 1.7 million bases of comparative DNA sequence were generated from 68 candidate genes within the QTL intervals, corresponding to a survey of over 36,000 amino acids. Eight central nervous system genes, located within these QTLs, were identified that contain a total of 36 changes in protein coding sequence. Some of these coding variants are likely to contribute to the phenotypic variation between ILS/ISS animals, including sensitivity to alcohol, providing specific new genetic targets potentially important to the neuronal actions of alcohol.     

Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping

Association studies are widely seen as the most promising approach for finding polymorphisms that influence genetically complex traits, such as common diseases and responses to their treatment. Considerable interest has therefore recently focused on the development of methods that efficiently screen genomic regions or whole genomes for gene variants associated with complex phenotypes. One key element in this search is the use of linkage disequilibrium to gain maximal information from typing a selected subset of highly informative single-nucleotide polymorphism (SNP) markers, now often called "tagging SNPs" (tSNPs). Probably the most common approach to linkage-disequilibrium gene mapping involves a three-step program: (1) characterization of the haplotype structure in candidate genes or genomic regions of interest, (2) identification of tSNPs sufficient to represent the most common haplotypes, and (3) typing of tSNPs in clinical material. Early definitions of tSNPs focused on the amount of haplotype diversity that they explained. To select tSNPs that would have maximal power in a genetic association study, however, we have developed optimization criteria based on the r2 measure of association and have compared these with other criteria based on the haplotype diversity. To evaluate the full program and to assess how well the selected tags are likely to perform, we have determined the haplotype structure and have assessed tSNPs in the SCN1A gene, an important candidate gene for sporadic epilepsy. We find that as few as four tSNPs are predicted to maintain a consistently high r2 value with all other common SNPs in the gene, indicating that the tags could be used in an association study with only a modest reduction in power relative to direct assays of all common SNPs. This implies that very large case-control studies can be screened for variation in hundreds of candidate genes with manageable experimental effort, once tSNPs are identified. However, our results also show that tSNPs identified in one population may not necessarily perform well in another, indicating that the preliminary study to identify tSNPs and the later case-control study should be performed in the same population. Our results also indicate that tSNPs will not easily identify discrepant SNPs, which lie on importantly discriminating but apparently short genealogical branches. This could significantly complicate tagging approaches for phenotypes influenced by variants that have experienced positive selection.     

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.