Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

A survey of airway responsiveness in 36 inbred mouse strains facilitates gene mapping studies and identification of quantitative trait loci

Airway hyper-responsiveness (AHR) is a critical phenotype of human asthma and animal models of asthma. Other studies have measured AHR in nine mouse strains, but only six strains have been used to identify genetic loci underlying AHR. Our goals were to increase the genetic diversity of available strains by surveying 27 additional strains, to apply haplotype association mapping to the 36-strain survey, and to identify new genetic determinants for AHR. We derived AHR from the increase in airway resistance in females subjected to increasing levels of methacholine concentrations. We used haplotype association mapping to identify associations between AHR and haplotypes on chromosomes 3, 5, 8, 12, 13, and 14. And we used bioinformatics techniques to narrow the identified region on chromosome 13, reducing the region to 29 candidate genes, with 11 of considerable interest. Our combined use of haplotype association mapping with bioinformatics tools is the first study of its kind for AHR on these 36 strains of mice. Our analyses have narrowed the possible QTL genes and will facilitate the discovery of novel genes that regulate AHR in mice.     

Two quantitative trait loci for prepulse inhibition of startle identified on mouse chromosome 16 using chromosome substitution strains

Prepulse inhibition (PPI) of acoustic startle is a genetically complex quantitative phenotype of considerable medical interest due to its impairment in psychiatric disorders such as schizophrenia. To identify quantitative trait loci (QTL) involved in mouse PPI, we studied mouse chromosome substitution strains (CSS) that each carry a homologous chromosome pair from the A/J inbred strain on a host C57BL/6J inbred strain background. We determined that the chromosome 16 substitution strain has elevated PPI compared to C57BL/6J (P = 1.6 x 10(-11)), indicating that chromosome 16 carries one or more PPI genes. QTL mapping using 87 F(2) intercross progeny identified two significant chromosome 16 loci with LODs of 3.9 and 4.7 (significance threshold LOD is 2.3). The QTL were each highly significant independently and do not appear to interact. Sequence variation between B6 and A/J was used to identify strong candidate genes in the QTL regions, some of which have known neuronal functions. In conclusion, we used mouse CSS to rapidly and efficiently identify two significant QTL for PPI on mouse chromosome 16. The regions contain a limited number of strong biological candidate genes that are potential risk genes for psychiatric disorders in which patients have PPI impairments.     

Finding the molecular basis of quantitative traits: successes and pitfalls

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms.     

From QTL to QTN: Candidate Gene Set Approach and a Case Study in Porcine IGF1-FoxO Pathway

Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.     

Genetic and Molecular Basis of QTL of Diabetes in Mouse: Genes and Polymorphisms

A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions.Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes. Key Words: Quantitative trait loci, type 1 diabetes, insulin-dependent diabetes mellitus (IDDM), candidate gene, polymorphism, mouse.     

Identification of candidate genes in the type 2 diabetes modifier locus using expression QTL

To identify new genetic determinants relevant to type 2 diabetes (T2D), diabetic F2 progeny were generated by intercrossing F1 mice obtained from a cross of BKS.Cg-Lepr(db)+/+m and DBA/2, and T2D-related phenotypes were measured. In the F2 population, increased susceptibility to diabetes and obesity was observed. We also detected the major quantitative trait loci (QTL) modifying the severity of diabetes on chromosome 9, where peaks of logarithm of odds (LOD) overlapped for three traits. To identify candidate genes in the QTL intervals, we combined "expression QTL" (eQTL), taking mRNA levels as quantitative traits, and "interstrain sequence variations, including cSNPs." As a result, four genes were identified from cosegregation of clinical QTL with eQTL and 13 genes were found from interstrain cSNPs as candidates in the T2D modifier QTL. Our combined approach shows the acceleration of the discovery of candidate genes in the QTL of interest, spanning several megabases.     

New quantitative trait loci that regulate wound healing in an intercross progeny from DBA/1J and 129×1/SvJ inbred strains of mice

Wound healing/regeneration mouse models are few, and studies performed have mainly utilized crosses between MRL/MPJ (a good healer) and SJL/J (a poor healer) or MRL/lpr (a good healer) and C57BL/6J (a poor healer). Wound healing is a complex trait with many genes involved in the expression of the phenotype. Based on data from previous studies that common and additional quantitative trait loci (QTL) were identified using different crosses of inbred strains of mice for various complex traits, we hypothesized that a new cross would identify common and additional QTL, unique modes of inheritance, and interacting loci, which are responsible for variation in susceptibility to fast wound healing. In this study, we crossed DBA/1J (DBA, a good healer) and 129/SvJ (129, a poor healer) and performed a genome-wide scan using 492 (DBA×129) F2 mice and 98 markers to identify QTL that regulate wound healing/regeneration. Four QTL on chromosomes 1, 4, 12, and 18 were identified which contributed toward wound healing in F2 mice and accounted for 17.1% of the phenotypic variation in ear punch healing. Surprisingly, locus interactions contributed to 55.7% of the phenotype variation in ear punch healing. In conclusion, we have identified novel QTL and shown that minor interacting loci contribute significantly to wound healing in DBA×129 mice cross. The authors Masinde, Li, and Nguyen contributed equally to this article.     

Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster

For insects, temperature is a major environmental variable that can influence an individual's behavioral activities and fitness. Drosophila melanogaster is a cosmopolitan species that has had great success in adapting to and colonizing diverse thermal niches. This adaptation and colonization has resulted in complex patterns of genetic variation in thermotolerance phenotypes in nature. Although extensive work has been conducted documenting patterns of genetic variation, substantially less is known about the genomic regions or genes that underlie this ecologically and evolutionarily important genetic variation. To begin to understand and identify the genes controlling thermotolerance phenotypes, we have used a mapping population of recombinant inbred (RI) lines to map quantitative trait loci (QTL) that affect variation in both heat- and cold-stress resistance. The mapping population was derived from a cross between two lines of D. melanogaster (Oregon-R and 2b) that were not selected for thermotolerance phenotypes, but exhibit significant genetic divergence for both phenotypes. Using a design in which each RI line was backcrossed to both parental lines, we mapped seven QTL affecting thermotolerance on the second and third chromosomes. Three of the QTL influence cold-stress resistance and four affect heat-stress resistance. Most of the QTL were trait or sex specific, suggesting that overlapping but generally unique genetic architectures underlie resistance to low- and high-temperature extremes. Each QTL explained between 5 and 14% of the genetic variance among lines, and degrees of dominance ranged from completely additive to partial dominance. Potential thermotolerance candidate loci contained within our QTL regions are identified and discussed.     

Understanding quantitative genetic variation

Until recently, it was impracticable to identify the genes that are responsible for variation in continuous traits, or to directly observe the effects of their different alleles. Now, the abundance of genetic markers has made it possible to identify quantitative trait loci (QTL)--the regions of a chromosome or, ideally, individual sequence variants that are responsible for trait variation. What kind of QTL do we expect to find and what can our observations of QTL tell us about how organisms evolve? The key to understanding the evolutionary significance of QTL is to understand the nature of inherited variation, not in the immediate mechanistic sense of how genes influence phenotype, but, rather, to know what evolutionary forces maintain genetic variability.     

Mapping of a Chromosome 12 Region Associated with Airway Hyperresponsiveness in a Recombinant Congenic Mouse Strain and Selection of Potential Candidate Genes by Expression and Sequence Variation Analyses

In a previous study we determined that BcA86 mice, a strain belonging to a panel of AcB/BcA recombinant congenic strains, have an airway responsiveness phenotype resembling mice from the airway hyperresponsive A/J strain. The majority of the BcA86 genome is however from the hyporesponsive C57BL/6J strain. The aim of this study was to identify candidate regions and genes associated with airway hyperresponsiveness (AHR) by quantitative trait locus (QTL) analysis using the BcA86 strain. Airway responsiveness of 205 F2 mice generated from backcrossing BcA86 strain to C57BL/6J strain was measured and used for QTL analysis to identify genomic regions in linkage with AHR. Consomic mice for the QTL containing chromosomes were phenotyped to study the contribution of each chromosome to lung responsiveness. Candidate genes within the QTL were selected based on expression differences in mRNA from whole lungs, and the presence of coding non-synonymous mutations that were predicted to have a functional effect by amino acid substitution prediction tools. One QTL for AHR was identified on Chromosome 12 with its 95% confidence interval ranging from 54.6 to 82.6 Mbp and a maximum LOD score of 5.11 (p = 3.68×10⁻³). We confirmed that the genotype of mouse Chromosome 12 is an important determinant of lung responsiveness using a Chromosome 12 substitution strain. Mice with an A/J Chromosome 12 on a C57BL/6J background have an AHR phenotype similar to hyperresponsive strains A/J and BcA86. Within the QTL, genes with deleterious coding variants, such as Foxa1, and genes with expression differences, such as Mettl21d and Snapc1, were selected as possible candidates for the AHR phenotype. Overall, through QTL analysis of a recombinant congenic strain, microarray analysis and coding variant analysis we identified Chromosome 12 and three potential candidate genes to be in linkage with airway responsiveness.     

Advances in QTL mapping in pigs

Over the past 15 years advances in the porcine genetic linkage map and discovery of useful candidate genes have led to valuable gene and trait information being discovered. Early use of exotic breed crosses and now commercial breed crosses for quantitative trait loci (QTL) scans and candidate gene analyses have led to 110 publications which have identified 1,675 QTL. Additionally, these studies continue to identify genes associated with economically important traits such as growth rate, leanness, feed intake, meat quality, litter size, and disease resistance. A well developed QTL database called PigQTLdb is now as a valuable tool for summarizing and pinpointing in silico regions of interest to researchers. The commercial pig industry is actively incorporating these markers in marker-assisted selection along with traditional performance information to improve traits of economic performance. The long awaited sequencing efforts are also now beginning to provide sequence available for both comparative genomics and large scale single nucleotide polymorphism (SNP) association studies. While these advances are all positive, development of useful new trait families and measurement of new or underlying traits still limits future discoveries. A review of these developments is presented.     

There is more to tomato fruit colour than candidate carotenoid genes

Determining gene sequences responsible for complex phenotypes has remained a major objective in modern biology. The candidate gene approach is attempting to link, through mapping analysis, sequences that have a known functional role in the measured phenotype with quantitative trait loci (QTL) that are responsible for the studied variation. To explore the potential of the candidate approach for complex traits we conducted a mapping analysis of QTL for the intensity of the red colour of the tomato fruit (mainly lycopene) and for probes associated with the well-characterized carotenoid biosynthesis pathway. Seventy-five tomato introgression lines (ILs), each containing a single homozygous RFLP-defined chromosome segment from the green-fruited species Lycopersicon pennellii delimited 107 marker-defined mapping bins. Three of the bins resolved known qualitative colour mutations for yellow (r) and orange (B and Del) fruits resulting from variation in specific carotenoid biosynthesis genes. Based on trials in different environments, 16 QTL that modified the intensity of the red colour of ripe fruit were assigned to bins. Candidate sequences associated with the carotenoid biosynthesis pathway were mapped to 23 loci. Only five of the QTL co-segregated with the same bins that contained candidate genes - a number that is expected by chance alone. Furthermore, similar map location of a QTL and a candidate is far from a direct causative relationship between a gene and a phenotype. This study highlights the wealth and complexity of the variation present in the genus Lycopersicon that could be employed for basic research and genetic improvement of fruit colour in tomato.     

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.     

An Integrative Genomic Analysis of the Superior Fecundity Phenotype in QSi5 Mice

Laboratory inbred mouse models are a valuable resource to identify quantitative trait loci (QTL) for complex reproductive performance traits. Advances in mouse genomics and high density single nucleotide polymorphism mapping has enabled genome-wide association studies to identify genes linked with specific phenotypes. Gene expression profiles of reproductive tissues also provide potentially useful information for identifying genes that play an important role. We have developed a highly fecund inbred strain, QSi5, with accompanying genotyping for comparative analysis of reproductive performance. Here we analyzed the QSi5 phenotype using a comparative analysis with fecundity data derived from 22 inbred strains of mice from the Mouse Phenome Project, and integration with published expression data from mouse ovary development. Using a haplotype association approach, 400 fecundity-associated regions (FDR < 0.05) with 499 underlying genes were identified. The most significant associations were located on Chromosomes 14, 8, and 6, and the genes underlying these regions were extracted. When these genes were analyzed for expression in an ovarian development profile (GSE6916) several distinctive co-expression patterns across each developmental stage were identified. The genetic analysis also refined 21 fecundity associated intervals on Chromosomes 1, 6, 9, 13, and 17 that overlapped with previously reported reproductive performance QTL. The combined use of phenotypic and in silico data with an integrative genomic analysis provides a powerful tool for elucidating the molecular mechanisms underlying fecundity.     

Investigation of Obesity Candidate Genes On Porcine Fat Deposition Quantitative Trait Loci Regions

Objectives: To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity‐related phenotypes. Research Methods and Procedures: Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. Results: Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene‐gene interactions were also studied. Discussion: Comparative analysis of obesity‐related genes in the pig is not only important for development of marker‐assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.     

Angiogenesis QTL on Mouse Chromosome 8 Colocalizes with Differential β-Defensin Expression

Identification of genetic factors that modify complex traits is often complicated by gene-environment interactions that contribute to the observed phenotype. In model systems, the phenotypic outcomes quantified are typically traits that maximize observed variance, which in turn, should maximize the detection of quantitative trait loci (QTL) in subsequent mapping studies. However, when the observed trait is dependent on multiple interacting factors, it can complicate genetic analysis, reducing the likelihood that the modifying mutation will ultimately be found. Alternatively, by focusing on intermediate phenotypes of a larger condition, we can reduce a model’s complexity, which will, in turn, limit the number of QTL that contribute to variance. We used a novel method to follow angiogenesis in mice that reduces environmental variance by measuring endothelial cell growth from culture of isolated skin biopsies that varies depending on the genetic source of the tissue. This method, in combination with a backcross breeding strategy, is intended to reduce genetic complexity and limit the phenotypic effects to fewer modifier loci. We determined that our approach was an efficient means to generate recombinant progeny and used this cohort to map a novel s.c. angiogenesis QTL to proximal mouse chromosome (Chr.) 8 with suggestive QTL on Chr. 2 and 7. Global mRNA expression analysis of samples from parental reference strains revealed β-defensins as potential candidate genes for future study.