Biomedical subjects

P Uimari

Publications and source records attributed to P Uimari.

5 recordsLinked to original sources

PubMed1998

Mapping quantitative trait loci for milk production and health of dairy cattle in a large outbred pedigree.

Quantitative trait loci (QTL) affecting milk production and health of dairy cattle were mapped in a very large Holstein granddaughter design. The analysis included 1794 sons of 14 sires and 206 genetic markers distributed across all 29 autosomes and flanking an estimated 2497 autosomal cM using Kosambi's mapping function. All families were analyzed jointly with least-squares (LS) and variance components (VC) methods. A total of 6 QTL exceeding approximate experiment-wise significance thresholds, 24 QTL exceeding suggestive thresholds, and 34 QTL exceeding chromosome-wise thresholds were identified. Significance thresholds were determined via data permutation (for LS analysis) and chi-square distribution (for VC analysis). The average bootstrap confidence interval for the experiment-wise significant QTL was 48 cM. Some chromosomes harbored QTL affecting several traits, and these were always in coupling phase, defined by consistency with genetic correlations among traits. Chromosome 17 likely harbors 2 QTL affecting milk yield, and some other chromosomes showed some evidence for 2 linked QTL affecting the same trait. In each of these cases, the 2 QTL were in repulsion phase in those families appearing to be heterozygous for both QTL, a finding which supports the build-up of linkage disequilibrium due to selection.

Analysis of Variance↗

PubMed1997

Sensitivity of segregation analysis to data structure and transformation: a case study of trypanotolerance in mice.

Sensitivity of segregation analysis for data structure and data transformation was studied using data from two trials in which mice were challenged at three months of age with a cloned isolate of Trypanosoma congolense and survival time was recorded. Data included records from three inbred strains (C57BL/6 (tolerant), A/J, and BALB/c (both susceptible)) and their crosses. Data were standardized and normalized using a modified power transformation. Segregation analysis was applied to both untransformed and transformed data to determine the genetic inheritance of trypanotolerance in these mice. Data from the two trials were analysed separately and combined. Four genetic models were compared; a one locus model, a polygenic model, a mixed model with common variance, and a mixed model with different variances for each major genotype. Even though the separate data sets and the combined data set all supported the hypothesis of a major gene (or a tightly linked cluster of genes) with different variances within each genotype, parameter estimates were highly sensitive to data transformation and several sets of parameter estimates gave similar likelihood values because of high dependency between parameters. Based on the results segregation analysis can be very sensitive to data structure in a crossbreeding design and to data transformation. Interpretation of the results can be misleading if the entire parameter space is not studied carefully.

Animals↗

PubMed1997

Mapping-linked quantitative trait loci using Bayesian analysis and Markov chain Monte Carlo algorithms.

A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL, and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL, affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL, on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL, and no additional or an unlinked QTL.

Algorithms↗

PubMed1997

Advances in statistical methods to map quantitative trait loci in outbred populations.

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.

Bayes Theorem↗

PubMed1996

The use of multiple markers in a Bayesian method for mapping quantitative trait loci.

Information on multiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitative trait loci (QTL). Bayesian parameter estimation and hypothesis testing were implemented via Markov chain Monte Carlo algorithms. Variables sampled were the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage or nonlinkage, and the parameters. The parameter vector included allele frequencies at the markers and the QTL, map distances of the markers and the QTL, QTL substitution effect, and polygenic and residual variances. The criterion for QTL detection was the marginal posterior probability of a QTL being located on the chromosome carrying the markers. The method was evaluated empirically by analyzing simulated granddaughter designs consisting of 2000 sons, 20 related sires, and their ancestors.

Algorithms↗