Detection and Mapping of Quantitative Trait Loci that Determine Responsiveness

Exposure to 70% N2O evokes a robust antinociceptive effect in C57BL/6 (B6) but not in DBA/2 (D2) inbred mice. This study was conducted to identify quantitative trait loci (QTL) in the mouse genome that might determine responsiveness to N2O. Offspring from the F2 generation bred from B6 and D2 progenitors exhibited a broad range of responsiveness to N2O antinociception as determined by the acetic acid-induced abdominal constriction test. QTL analysis was then used to dissect this continuous trait distribution into component loci, and to map them to broad chromosomal regions. To this end, 24 spleens were collected from each of the following four groups: male and female F2 mice responding to 70% N2O in oxygen with 100% response (high-responders); and male and female F2 mice responding with 0% response (low-responders). Genomic DNA was extracted from the spleens and genotyped with simple sequence length polymorphism MapPairs markers. Findings were combined with findings from the earlier QTL analysis from BXD recombinant inbred mice [Brain Res 725 (1996) 23]. Combined results revealed two significant QTL that influence responsiveness to nitrous oxide on proximal chromosome 2 and distal chromosome 5, and one suggestive QTL on midchromosome 18. The chromosome 2 QTL was evident only in males. A significant interaction was found between a locus on chromosome 6 and another on chromosome 13 with a substantial effect on N2O antinociception

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities

Genetic analysis of safflower domestication.

BackgroundSafflower (Carthamus tinctorius L.) is an oilseed crop in the Compositae (a.k.a. Asteraceae) that is valued for its oils rich in unsaturated fatty acids. Here, we present an analysis of the genetic architecture of safflower domestication and compare our findings to those from sunflower (Helianthus annuus L.), an independently domesticated oilseed crop within the same family.We mapped quantitative trait loci (QTL) underlying 24 domestication-related traits in progeny from a cross between safflower and its wild progenitor, Carthamus palaestinus Eig. Also, we compared QTL positions in safflower against those that have been previously identified in cultivated x wild sunflower crosses to identify instances of colocalization.ResultsWe mapped 61 QTL, the vast majority of which (59) exhibited minor or moderate phenotypic effects. The two large-effect QTL corresponded to one each for flower color and leaf spininess. A total of 14 safflower QTL colocalized with previously reported sunflower QTL for the same traits. Of these, QTL for three traits (days to flower, achene length, and number of selfed seed) had cultivar alleles that conferred effects in the same direction in both species.ConclusionsAs has been observed in sunflower, and unlike many other crops, our results suggest that the genetics of safflower domestication is quite complex. Moreover, our comparative mapping results indicate that safflower and sunflower exhibit numerous instances of QTL colocalization, suggesting that parallel trait transitions during domestication may have been driven, at least in part, by parallel genotypic evolution at some of the same underlying genes

Bayesian multi-QTL mapping for growth curve parameters

Background Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a Bayesian algorithm. Results For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five. Conclusions Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one

Rice root genetic architecture: Meta-analysis from a drought QTL database

During the last 10 years, a large number of quantitative trait loci (QTLs) controlling rice root morphological parameters have been detected in several mapping populations by teams interested in improving drought resistance in rice. Compiling these data could be extremely helpful in identifying candidate genes by positioning consensus QTLs with more precision through meta-QTL analysis. We extracted information from 24 published papers on QTLs controlling 29 root parameters including root number, maximum root length, root thickness, root/ shoot ratio, and root penetration index. A web-accessible database of 675 root QTLs detected in 12 populations was constructed. This database includes also all QTLs for drought resistance traits in rice published between 1995 and 2007. The physical position on the pseudochromosomes of the markers flanking each QTL was determined. An overview of the number of root QTLs in 5-Mb segments covering the whole genome revealed the existence of "hot spots," The 32 trait × chromosome combinations comprising six or more QTLs were subjected to a meta-QTL analysis using the software package MetaQTL. The method enabled us both to determine the likely number of true QTLs in these areas using an Akaike information criterion and to estimate their position. The meta-QTL confidence intervals were notably reduced and, for the smallest ones, encompassed only a few genes. (Résumé d'auteur

Creation of a Computational Pipeline to Extract Genes from Quantitative Trait Loci for Diabetes and Obesity

Type 2 Diabetes is a disease of relative insulin deficiency resulting from a combination of insulin resistance and decreased beta-cell function. Over the past several years, over 60 genes have been identified for Type 2 Diabetes in human genome-wide association studies (GWAS). It is important to understand the genetics involved with Type 2 diabetes in order to improve treatment and understand underlying molecular mechanisms. Heterogeneous stock (HS) rats are derived from 8 inbred founder strains and are powerful tools for genetic studies because they provide a basis for high resolution mapping of quantitative trait loci (QTL) in a relatively short time period. By measuring diabetic traits in 1090 HS male rats and genotyping 10K single nucleotide polymorphisms (SNPs) within these rats, Dr. Solberg Woods\u27 lab conducted genetic analysis to identify 85 QTL for diabetes and adiposity traits. To identify candidate genes within these QTL, we propose creation of a bioinformatics pipeline that combines general gene information, information from the rat genome database including disease portals and Variant Visualizer as well as the Attie Diabetes Expression Database. My project has involved writing code to pull data from these databases to determine which genes within each QTL are potential candidate genes. I have scripted the code to analyze genes within a single QTL or multiple QTL simultaneously. The resulting output is a single excel file for each QTL, listing all genes that are found in the disease portals, all genes that have a highly conserved non-synonymous variant change and all genes that are differentially expressed in the Attie database. The program also highlights genes that are found in all three categories. After creating the pipeline, I ran the program for 85 QTL identified in my laboratory. The program identified 63 high priority candidate genes for future follow-up. This work has helped my laboratory rapidly identify candidate genes for type 2 diabetes and obesity. In the future, the code can be modified to identify candidate genes within QTL for any complex trait

Fast Genome-Wide QTL Analysis Using Mendel

Pedigree GWAS (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large scale genome-wide QTL analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both, (b) is highly efficient in both run time and memory requirement, (c) accommodates both univariate and multivariate traits, (d) works for autosomal and x-linked loci, (e) correctly deals with missing data in traits, covariates, and genotypes, (f) allows for covariate adjustment and constraints among parameters, (g) uses either theoretical or SNP-based empirical kinship matrix for additive polygenic effects, (h) allows extra variance components such as dominant polygenic effects and household effects, (i) detects and reports outlier individuals and pedigrees, and (j) allows for robust estimation via the $t$-distribution. The current paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. We analyzed simulated and real phenotypes for both family and random sample data sets. For instance, when jointly testing the 8 longitudinally measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) traits, it takes Mendel 78 minutes on a standard laptop computer to read, quality check, and analyze a data set with 849 individuals and 8.3 million SNPs. Genome-wide eQTL analysis of 20,643 expression traits on 641 individuals with 8.3 million SNPs takes 30 hours using 20 parallel runs on a cluster. Mendel is freely available at \url{http://www.genetics.ucla.edu/software}

Multivariate meta-analysis of QTL mapping studies

A large number of quantitative trait loci (QTLs) for milk production and quality traits in dairy cattle has been reported in literature. The large amount of information available could be exploited by meta-analyses to draw more general conclusions from results obtained in different experimental conditions (animals, statistical methodologies). QTL meta-analyses have been carried out to estimate the distribution of QTL effects in livestock and to find consensus on QTL position. In this study, multivariate dimension reduction techniques are used to analyse a database of dairy cattle QTL published results, in order to extract latent variables able to characterise the research. A total of 92 papers by 72 authors were found on 25 scientific Journals for the period January 1995-February 2008. More than thirty parameters were picked up from the articles. To overcome the problem of different map location, the flanking markers were mapped on release 4.1 of the Bos taurus genome sequence (www.ensembl. org). Their position was retrieved from public databases and, when absent, was calculated in silico by blasting (http://blast.wustl.edu/) the markers’ nucleotide sequence against the genomic sequence. Records were discarded if flanking markers or P-values were not available. After these edits, the final archive consisted of 1,162 records. Seven selected variables were analysed both with the Factor Analysis (FA), combined with the varimax rotation technique, and Principal Component Analysis (PCA). FA was able to explain 68% of the original variability with 3 latent factors: the first factor extracted was highly associated (factor loading of 0.98) to marker location along the chromosome and could be considered as a marker map index; the second factor showed factor loadings of 0.74 and 0.84 related to the variable number of animals involved and year of the experiment, respectively, and it can be regarded as an indicator of the dimension of the study; the third factor was correlated to the significance level of the statistical test (0.78), number of families (0.63), and, negatively, to the marker density (-0.43). It can be named as index of power of the experiment. Same patterns can be observed in the eigenvectors of PCA. Four PCs were able to explain about 80% of the original variance. The first two PCs basically underlined accurately the same structure found with the first two factors in FA, whereas PC3 and PC4 summarized the structure of F3. The score that each QTL gets on each Factor or PC could be useful to classify the original QTL records and make them more comparable once that the redundancy of information has been removed