qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

Background qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. Findings Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. Conclusion qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

Modelling dominance in a flexible intercross analysis

The aim of this paper is to develop a flexible model for analysis of quantitative trait loci (QTL) in outbred line crosses, which includes both additive and dominance effects. Our flexible intercross analysis (FIA) model accounts for QTL that are not fixed within founder lines and is based on the variance component framework. Genome scans with FIA are performed using a score statistic, which does not require variance component estimation. RESULTS: Simulations of a pedigree with 800 F2 individuals showed that the power of FIA including both additive and dominance effects was almost 50% for a QTL with equal allele frequencies in both lines with complete dominance and a moderate effect, whereas the power of a traditional regression model was equal to the chosen significance value of 5%. The power of FIA without dominance effects included in the model was close to those obtained for FIA with dominance for all simulated cases except for QTL with overdominant effects. A genome-wide linkage analysis of experimental data from an F2 intercross between Red Jungle Fowl and White Leghorn was performed with both additive and dominance effects included in FIA. The score values for chicken body weight at 200 days of age were similar to those obtained in FIA analysis without dominance. CONCLUSION: We have extended FIA to include QTL dominance effects. The power of FIA was superior, or similar, to standard regression methods for QTL effects with dominance. The difference in power for FIA with or without dominance is expected to be small as long as the QTL effects are not overdominant. We suggest that FIA with only additive effects should be the standard model to be used, especially since it is more computationally efficient.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Familial and genomic analyses of postural changes in systolic and diastolic blood pressure

The physiological adaptation to the erect posture involves integrated neural and cardiovascular responses that might be determined by genetic factors. We examined the familial- and individual-specific components of variance for postural changes in systolic and diastolic blood pressure in 767 volunteer nuclear adult families from the Victorian Family Heart Study. In 274 adult sibling pairs, we made a genome-wide scan using 400 markers for quantitative trait loci linked with the postural changes in systolic and diastolic pressures. Overall, systolic pressure did not change on standing, but there was considerable variation in this phenotype (SD=8.1 mm Hg). Familial analyses revealed that 25% of the variance of change in systolic pressure was attributable to genetic factors. In contrast, diastolic pressure increased by 6.3 mm Hg (SD=7.0 mm Hg) on standing and there was no evidence of contributory genetic factors. Multipoint quantitative genome linkage mapping suggested evidence (Z=3.2) of linkage of the postural change in systolic pressure to chromosome 12 but found no genome-wide evidence of linkage for the change in diastolic pressure. These findings suggest that genetic factors determine whether systolic pressure decreases or increases when one stands, possibly as the result of unidentified alleles on chromosome 12. The genetics of postural changes in systolic blood pressure might reflect the general buffering function of the baroreflex; thereby, the predisposition to sudden decreases or increases in systolic pressure might cause postural hypotension or vessel wall disruption, respectively.

Contributions and perspectives of chicken genomics in Brazil: from biological model to export commodity

Chicken is one of the most important sources of animal protein for human consumption, and breeding programmes have been responsible for constant improvements in production efficiency and product quality. Furthermore, chicken has largely contributed to fundamental discoveries in biology for the last 100 years. In this article we review recent developments in poultry genomics and their contribution to adding functional information to the already existing structural genomics, including the availability of the complete genome sequence, a comprehensive collection of mRNA sequences ( ESTs), microarray platforms, and their use to complement QTL mapping strategies in the identification of genes that underlie complex traits. Efforts of the Brazilian Poultry Genomics Programme in this area resulted in generation of a resource population, which was used for identification of Quantitative Trait Loci ( QTL) regions, generation of ESTs and candidate gene studies that contributed to furthering our understanding of the complex biological processes involved in growth and muscular development in chicken.

Genetic linkage maps of chicken chromosomes 6, 7, 8, 11 and 13 from a Brazilian resource population

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Physical mapping of the Nile tilapia (Oreochromis niloticus) genome by fluorescent in situ hybridization of repetitive DNAs to metaphase chromosomes - a review

The Nile tilapia (Oreochromis niloticus) has received increasing scientific interest over the past few decades for two reasons: first, tilapia is an enormously important species in aquaculture worldwide, especially in regions where there is a chronic shortage of animal protein; and second, this teleost fish belongs to the fascinating group of cichlid fishes that have undergone a rapid and extensive radiation of much interest to evolutionary biologists. Currently, studies based on physical and genetic mapping of the Nile tilapia genome offer the best opportunities for applying genomics to such diverse questions and issues as phylogeography, isolation of quantitative trait loci involved in behaviour, morphology, and disease, and overall improvement of aquacultural stocks. In this review, we have integrated molecular cytogenetic data for the Nile tilapia describing the chromosomal location of the repetitive DNA sequences, satellite DNAs, telomeres, 45S and 5S rDNAs, and the short and long interspersed nucleotide elements [short interspersed nuclear elements (SINEs) and long interspersed nuclear elements (LINEs)], and provide the beginnings of a physical genome map for this important teleost fish. (C) 2004 Elsevier B.V. All rights reserved.

Genetic linkage map of chicken chromosome 1 from a Brazilian resource population

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A deterministic simulation study of embryo marker-assisted selection for age at first calving in Nellore (Bos indicus) beef cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapping QTLs for aluminum tolerance in maize

Aluminum toxicity is one of the major constraints for plant development in acid soils, limiting food production in many countries. Cultivars genetically adapted to acid soils may offer an environmental compatible solution, providing a sustainable agriculture system. The aim of this work was to identify genomic regions associated with Al tolerance in maize, and to quantify the genetic effects on the phenotypic variation. A population of 168 F-3:4 families derived from a cross between two contrasting maize inbred lines for Al tolerance was evaluated using the NSRL and RSRL parameters in nutrient solution containing toxic level of aluminum. Variance analyses indicated that the NSRL was the most reliable phenotypic index to measure Al tolerance in the population, being used for further QTL mapping analysis. RFLP and SSR markers were selected for bulked segregant analysis, and additional SSR markers, flanking the polymorphisms of interest, were chosen in order to saturate the putative target regions. Seven linkage groups were constructed using 17 RFLP and 34 SSR markers. Five QTLs were mapped on chromosomes 2, 6 and 8, explaining 60% of the phenotypic variation. QTL(4) and marker umc043 were located on chromosomes 8 and 5, close to genes encoding for enzymes involved in the organic acids synthesis pathways, a widely proposed mechanism for Al tolerance in plants. QTL(2) was mapped in the same region as Alm2, also associated with Al tolerance in maize. In addition, dominant and additive effects were important in the control of this trait in maize.

Mapping QTLs on chicken chromosome 1 for performance and carcass traits in a broiler x layer cross

With the objective of mapping quantitative trait loci (QTLs) for performance and carcass traits, an F-2 chicken population was developed by crossing broiler and layer lines. A total of 2063 F-2 chicks in 21 full-sib families were reared as broilers and slaughtered at 42 days of age. Seventeen performance and carcass traits were measured. Parental (F-0) and F-1 individuals were genotyped with 80 microsatellites from chicken chromosome 1 to select informative markers. Thirty-three informative markers were used for selective genotyping of F-2 individuals with extreme phenotypes for body weight at 42 days of age (BW42). Based on the regions identified by selective genotyping, seven full-sib families (649 F-2 chicks) were genotyped with 26 markers. Quantitative trait loci affecting body weight, feed intake, carcass weight, drums and thighs weight and abdominal fat weight were mapped to regions already identified in other populations. Quantitative trait loci for weights of gizzard, liver, lungs, heart and feet, as well as length of intestine, not previously described in the literature were mapped on chromosome 1. This F-2 population can be used to identify novel QTLs and constitutes a new resource for studies of genes related to growth and carcass traits in poultry.

A Bayesian approach for constructing genetic maps when markers are miscoded

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.

QTL identification for tolerance to fruit set in tomato by FAFLP markers

This study aimed to detect quantitative trait loci (QTL) by fALP (Fluorescent Amplified Fragment Length Polymorphism) markers associated to the trait tomato fruit set at high temperatures. A biparental cross between line Jab-95 (heat-tolerant) and cultivar Caribe (heat-susceptible) was made. A total of 192 plants of the F2 generation were evaluated, generating 172 polymorphic markers through six primer combinations previously identified by the Bulked Segregant Analysis technique. To construct the genetic map, 106 of the 172 markers that segregated in the expected Mendelian segregation proportion (3:1) were used. The map covered 1191.46 cM of the genome. Six trait-linked QTL were identified in the analysis of simple markers and three others by the interval-mapping methodology. These results could be highly useful in improvement programs, since heat-tolerant plants can be selected rapidly, which improves tomato fruit set.

Associations between microsatellite markers and traits related to performance, carcass and organs in chickens

Associations between four microsatellite markers on chromosome 11 and five on chromosome 13 with performance, carcass and organs traits were investigated in chickens using a least-squares approach applied to single-marker analysis. Three hundred and twenty seven F 2 chickens from the EMBRAPA broiler×layer experimental population were evaluated for 16 traits: five related to performance, five to carcass and five to organs, plus the hematocrit. Two significance thresholds were considered: p<0.05 and p<0.0056; the last value resulted from the application of a multiple tests analyses correction. On chromosome 11, six associations (p<0.05) between the genotypes of two markers with four growth related and one carcass trait were found. On chromosome 13, six associations (p<0.05) between marker genotypes and three performance traits, eight associations (p<0.05) between marker genotypes and two carcass traits and eight associations (p<0.05) between marker genotypes and four organs traits were detected. These associations were indications of the presence of quantitative trait loci on these chromosomes, especially on chromosome 13. In this chromosome, the strongest evidence was for body weight at 41 days of age and percentage of carcass because the p-values exceeded the multiple test threshold (p<0.0056), but also for breast percentage and heart weight due to the large number of markers (four) on chromosome 13 associated with each one of these traits. These associations should be further investigated by interval mapping analyses to find QTL positions and to allow the estimation of their effects. © Asian Network for Scientific Information, 2009.