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At least 217 records · Page 12Linked to original sources
PubMed1996

Identity-by-descent mapping of recessive traits in livestock: application to map the bovine syndactyly locus to chromosome 15.

Twelve animals affected with syndactyly or mulefoot were sampled in the Dutch black-and-white cattle population. Analysis of the pedigree data reveal that all of these individuals traced back to a single acknowledged carrier founder individual. Between seven and nine generations separated the founder from its 12 affected descendents. The 12 affected offspring were genotyped for a battery of 213 microsatellites spanning the 29 bovine autosomes. The resulting genotypes were analyzed using a maximum likelihood approach searching for shared homozygous haplotypes among affected individuals. Three candidate regions for the syndactyly locus emerged from this initial screening. syndactyly was shown to map to one of these candidate regions on chromosome 15 by genotyping 29 additional individuals linking founder and affected offspring and performing a conventional linkage analysis with the LINKAGE programs. This study illustrates the potential of identity-by-descent mapping in livestock populations.

Animals↗
PubMed2005

Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452x'AC Domain'.

Relatively little is known about the genetic control of agronomic traits in common wheat (Triticum aestivum L.) compared with traits that follow Mendelian segregation patterns. A doubled-haploid population was generated from the cross RL4452x'AC Domain' to study the inheritance of the agronomic traits: plant height, time to maturity, lodging, grain yield, test weight, and 1000-grain weight. This cross includes the genetics of 2 western Canadian wheat marketing classes. Composite interval mapping was conducted with a microsatellite linkage map, incorporating 369 loci, and phenotypic data from multiple Manitoba environments. The plant height quantitative trait loci (QTLs), QHt.crc-4B and QHt.crc-4D, mapped to the expected locations of Rht-B1 and Rht-D1. These QTLs were responsible for most of the variation in plant height and were associated with other agronomic traits. An additional 25 agronomic QTLs were detected in the RL4452x'AC Domain' population beyond those associated with QHt.crc-4B and QHt.crc-4D. 'AC Domain' contributed 4 alleles for early maturity, including a major time to maturity QTL on 7D. RL4452 contributed 2 major alleles for increased grain yield at QYld.crc-2B and QYld.crc-4A, which are potential targets for marker-assisted selection. A key test weight QTL was detected on 3B and prominent 1000-grain weight QTLs were identified on 3D and 4A.

Chromosome Mapping↗
PubMed2006

Mapping quantitative trait loci associated with selenate tolerance in Arabidopsis thaliana.

Selenium is essential for many organisms, but is toxic at higher levels. To investigate the genetic basis of selenate tolerance in Arabidopsis thaliana, quantitative trait loci (QTL) associated with selenate tolerance in accessions Landsberg erecta and Columbia were mapped using recombinant inbred lines (RILs). The selenate tolerance index (TI(D10) = root growth + 30 microm selenate/root growth control x 100%) was fourfold higher for parental line Col-4 (59%) than for parent Ler-0 (15%). Among the 96 F8 RILs, TI(D10) ranged from 11 to 75% (mean 37%). Using composite interval mapping, three QTL were found on chromosomes 1, 3 and 5, which together explained 24% of variation in TI(D10) and 32% of the phenotypic variation for the difference in root length +/- Se (RL(D10)). Highly significant epistatic interactions between the QTL and markers on chromosome 2 explained additional variation for both traits. Potential candidate genes for Se tolerance in each of the QTL regions are discussed. These results offer insight into the genetic basis of selenate tolerance, and may be useful for identification of selenate-tolerance genes.

Arabidopsis↗
PubMed1998

Mapping quantitative trait loci using multiple families of line crosses.

To avoid a loss in statistical power as a result of homozygous individuals being selected as parents of a mapping population, one can use multiple families of line crosses for quantitative trait genetic linkage analysis. Two strategies of combining data are investigated: the fixed-model and the random-model strategies. The fixed-model approach estimates and tests the average effect of gene substitution for each parent, while the random-model approach treats each effect of gene substitution as a random variable and directly estimates and tests the variance of gene substitution. Extensive Monte Carlo simulations verify that the two strategies perform equally well, although the random model is preferable in combining data from a large number of families. Simulations also show that there may be an optimal sampling strategy (number of families vs. number of individuals per family) in which QTL mapping reaches its maximum power and minimum estimation error. Deviation from the optimal strategy reduces the efficiency of the method.

Chromosome Mapping↗
PubMed2003

[Mapping quantitative trait loci associated with rice grain shape based on an indica/japonica backcross population].

Appearance of rice grain represents a major character of rice quality in many rice-producing areas of the world, especially in hybrid rice production in China. In this study, we conducted a molecular marker-based genetic analysis of the traits that are determinants of the appearance quality of rice grains, including grain length, grain width and grain shape (measured as grain length to grain width ratio). Two typical indica/japonica varieties Balilla and Nantehao(NTH) were selected to construct Balilla/NTH//Balilla backcross population containing 142 individuals. In the population, grain length, grain width and grain shape all conform to the normal distribution with certain transgressive segregation. It can be deduced that all of three traits were controlled by some quantitative trait loci (QTLs). In order to explore the QTLs effect, number and location, a linkage map consisting of 108 SSR markers based on the backcross population was constructed, and QTLs mapping was carried out for grain length, grain width and grain shape. A QTL, qGL-12, was detected for grain length at the interval RM101-RM270 on chromosome 12, its additive effect was 0.26 mm, and can explain 16.7% genetic variation. As for grain width trait, two QTLs were found, qGW-2 located at RM154-RM211 interval on chromosome 2, and qGW-3 at interval RM257-RM175 on chromosome 3, accounting for 11.5% and 16.6% genetic variation, respectively. The alleles at qGW-2 and qGW-3 from parent Balilla can increase grain width by 0.10 mm and 0.12 mm. For grain shape, 3 QTLs, qLW-2, qLW-6 and qLW-7 were found, located on chromosome 2, 6, and 7, respectively. qLW-2 and qLW-7 had positive effect, and they can explain 12.7% and 18.3% genetic variation, while qLW-6 had negative effect and contributed 11.5% genetic variation to the backcross population. The prospects of application of linkage relationship between SSR marker and QTLs in marker based selection (MAS) in rice breeding, and the improvement of grain shape and rice appearance quality were discussed.

Chromosome Mapping↗
PubMed2002

Improved confidence intervals in quantitative trait loci mapping by permutation bootstrapping.

The nonparametric bootstrap approach is known to be suitable for calculating central confidence intervals for the locations of quantitative trait loci (QTL). However, the distribution of the bootstrap QTL position estimates along the chromosome is peaked at the positions of the markers and is not tailed equally. This results in conservativeness and large width of the confidence intervals. In this study three modified methods are proposed to calculate nonparametric bootstrap confidence intervals for QTL locations, which compute noncentral confidence intervals (uncorrected method I), correct for the impact of the markers (weighted method I), or both (weighted method II). Noncentral confidence intervals were computed with an analog of the highest posterior density method. The correction for the markers is based on the distribution of QTL estimates along the chromosome when the QTL is not linked with any marker, and it can be obtained with a permutation approach. In a simulation study the three methods were compared with the original bootstrap method. The results showed that it is useful, first, to compute noncentral confidence intervals and, second, to correct the bootstrap distribution of the QTL estimates for the impact of the markers. The weighted method II, combining these two properties, produced the shortest and less biased confidence intervals in a large number of simulated configurations.

Chromosome Mapping↗
PubMed1999

Assessing the feasibility of linkage disequilibrium methods for mapping complex traits: an initial screen for bipolar disorder loci on chromosome 18.

Linkage disequilibrium (LD) analysis has been promoted as a method of mapping disease genes, particularly in isolated populations, but has not yet been used for genome-screening studies of complex disorders. We present results of a study to investigate the feasibility of LD methods for genome screening using a sample of individuals affected with severe bipolar mood disorder (BP-I), from an isolated population of the Costa Rican central valley. Forty-eight patients with BP-I were genotyped for markers spaced at approximately 6-cM intervals across chromosome 18. Chromosome 18 was chosen because a previous genome-screening linkage study of two Costa Rican families had suggested a BP-I locus on this chromosome. Results of the current study suggest that LD methods will be useful for mapping BP-I in a larger sample. The results also support previously reported possible localizations (obtained from a separate collection of patients) of BP-I-susceptibility genes at two distinct sites on this chromosome. Current limitations of LD screening for identifying loci for complex traits are discussed, and recommendations are made for future research with these methods.

Bipolar Disorder↗
PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗
PubMed2005

[Methods of statistical genetics and use of database for genome information].

Knowledge and technology of bioinformatics have become inevitable for gene and genome research. Education and research in this field of science are not sufficient in Japan. There are two different approaches to trait mapping, the way by which traits are mapped on the genome. Thus, the knowledge-based approach uses functions of molecules while the statistics-based approach uses polymorphisms. Statistics-based approach uses two different methods, linkage analysis and analysis based on linkage disequilibrium. Various phenotypes are efficiently mapped on the genome using such methods. Recently, bioinformatic data base search is mostly performed using internet. Anyone can perform sequence-search, homology-search and SNP-search. Since such data bases change quickly, readers should access the databases themselves and be used to the procedures for them.

Computational Biology↗
PubMed2001

Bayesian methods for quantitative trait loci mapping based on model selection: approximate analysis using the Bayesian information criterion.

We describe an approximate method for the analysis of quantitative trait loci (QTL) based on model selection from multiple regression models with trait values regressed on marker genotypes, using a modification of the easily calculated Bayesian information criterion to estimate the posterior probability of models with various subsets of markers as variables. The BIC-delta criterion, with the parameter delta increasing the penalty for additional variables in a model, is further modified to incorporate prior information, and missing values are handled by multiple imputation. Marginal probabilities for model sizes are calculated, and the posterior probability of nonzero model size is interpreted as the posterior probability of existence of a QTL linked to one or more markers. The method is demonstrated on analysis of associations between wood density and markers on two linkage groups in Pinus radiata. Selection bias, which is the bias that results from using the same data to both select the variables in a model and estimate the coefficients, is shown to be a problem for commonly used non-Bayesian methods for QTL mapping, which do not average over alternative possible models that are consistent with the data.

Alleles↗
PubMed2006

Combined expression trait correlations and expression quantitative trait locus mapping.

Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein-coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.

Animals↗
PubMed2003

Mapping quantitative trait loci in the case of a spike in the phenotype distribution.

A common departure from the usual normality assumption in QTL mapping concerns a spike in the phenotype distribution. For example, in measurements of tumor mass, some individuals may exhibit no tumors; in measurements of time to death after a bacterial infection, some individuals may recover from the infection and fail to die. If an appreciable portion of individuals share a common phenotype value (generally either the minimum or the maximum observed phenotype), the standard approach to QTL mapping can behave poorly. We describe several alternative approaches for QTL mapping in the case of such a spike in the phenotype distribution, including the use of a two-part parametric model and a nonparametric approach based on the Kruskal-Wallis test. The performance of the proposed procedures is assessed via computer simulation. The procedures are further illustrated with data from an intercross experiment to identify QTL contributing to variation in survival of mice following infection with Listeria monocytogenes.

Computer Simulation↗
PubMed2002

Mapping of quantitative trait loci affecting quality and production traits in egg layers.

A mapping population segregating for egg quality traits was created by a line cross between two egg layer lines and screened by a genome scan. The F2 generation consisted of 307 hens, which were scored for egg quality and production traits. The mapping population was genotyped for 99 microsatellite loci, spanning nine macrochromosomes and five small linkage groups. The linkage maps were used in mapping QTL affecting 14 traits, by using multiple markers and a least-squares approach. We detected 14 genomewide significant and six suggestive QTL that were located on chromosomes 2, 3, 4, 5, and, 8 and sex chromosome Z. A significant QTL affecting egg white thinning was found on chromosome 2. For eggshell strength, a significant QTL was found on chromosome Z. For production traits, the most interesting area was on chromosome 4, where highly significant QTL effects were detected for BW, egg weight, and feed intake in the same area. The most significant QTL explains 25.8% of the phenotypic variance in F2 of body weight. An area affecting the age at first egg, egg weight, and the number of eggs was located on chromosome Z.

Animals↗
PubMed2001

Quantitative trait locus mapping: fishing strategy or replicable results?

Genomewide scan, as shown by recently published linkage studies on the behavior of mice or in psychiatry, provides evidence for replicated quantitative trait loci (QTL). Several studies of Alzheimer's disease have demonstrated the promise of moving from clinical diagnoses to biological signs for psychiatric investigations. Empirical studies also demonstrate that genetic background, differences in environment, epistatic effects, and definition of the phenotype (including psychiatric diagnosis) all actively contribute to limit the duplication of QTL.

Animals↗
PubMed2004

Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model-7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.

Animals↗
PubMed1997

Mapping quantitative trait loci for body weight on the X chromosome in mice. I. Analysis of a reciprocal F2 population.

Evidence of a large sex-linked effect accounting for 25% of the divergence between mouse lines selected for body weight has been described previously. A marker-based study was undertaken to determine the number and map positions of the putative X-linked quantitative trait loci (QTLs). An F2 population was generated from a reciprocal F1 between an inbred low line derived from the low selection line and the high selection line. To enable inference of marker-associated QTL effects on the X chromosome, an analytical technique was developed based on the multiple regression method of Haley and Knott. The analysis of data on 10 week weight indicated a single QTL of large effect situated at about 23 cM from the proximal end of the chromosome, with a peak LOD score of 24.4. The likelihood curve showed a single well-defined peak, and gave a 95% confidence interval for the QTL location of 8 cM. The estimates for the additive genotypic effects in males and females (half the differences between hemizygous males and between homozygous females) were 2.6 g in both cases, or 17% and 20% of the 10 week body weight in males and females respectively. Dominance effects in the females were found to be non-significant. No significant X-linked effect on carcass fat percentage was detected, but a single X-linked QTL appears to explain almost the entire X-linked body weight effect.

Adipose Tissue↗