Biomedical subjects

B Tier

Publications and source records attributed to B Tier.

7 recordsLinked to original sources

PubMed2001

Estimating genotypes with independently sampled descent graphs.

A method for estimating genotypic and identity-by-descent probabilities in complex pedigrees is described. The method consists of an algorithm for drawing independent genotype samples which are consistent with the pedigree and observed genotype. The probability distribution function for samples obtained using the algorithm can be evaluated up to a normalizing constant, and combined with the likelihood to produce a weight for each sample. Importance sampling is then used to estimate genotypic and identity-by-descent probabilities. On small but complex pedigrees, the genotypic probability estimates are demonstrated to be empirically unbiased. On large complex pedigrees, while the algorithm for obtaining genotype samples is feasible, importance sampling may require an infeasible number of samples to estimate genotypic probabilities with accuracy.

Algorithms↗

PubMed1998

Restricted maximum likelihood analysis of linkage between genetic markers and quantitative trait loci for a granddaughter design.

A REML for the estimation of location and variance of a single quantitative trait locus, together with polygenic and residual variance, is described for the analysis of a granddaughter design. The method is based on a mixed linear model that includes the allelic effects of the quantitative trait locus, which are assumed to be normally distributed. Information from four marker loci situated on a single chromosome was available to derive the covariances at the linked quantitative trait locus. A derivative-free algorithm is described that makes use of the specific structure of the granddaughter design. The procedure has been applied to simulated data for a granddaughter design with 50 grandsire families of 40 sires each. Error variance was 60, and total additive genetic variance equaled 40; the quantitative trait locus explained either 10 or 25% of the latter variance. The size of the marker bracket containing the quantitative trait locus was either 10 or 30 cM. The power of detecting the quantitative trait locus ranged from 19 to 99%. Estimates of total genetic variance and variance explained by the quantitative trait locus were found to be empirically unbiased. A small bias was found in location estimates, especially when markers were not fully informative. The accuracy of parameter estimates was greatly improved by the use of information from individual daughters.

Animals↗

PubMed1997

Assessing the efficiency of multiplicative mixed model equations to account for heterogeneous variance across herds in carcass scan traits from beef cattle.

Data (n = 2,658) from live animal ultrasonic measures from 17 Angus herds were used to evaluate a multiplicative mixed model that incorporates scaling factors to correct for across-herd heterogeneity of variance. Traits included were ribeye muscle area (EMA), surface fat at the P8 site (P8), surface fat between the 12th and 13th ribs (RIB12), and weight at scanning (WEIGHT). Cattle ranged in age from 501 to 698 d and represented 291 contemporary groups. Data were initially analyzed using single-trait, animal model, Method R procedures to estimate variance components and heritabilities (h2). These estimates were incorporated into a multiplicative mixed model that simultaneously estimates breeding values (EBV) and heterogeneity factors. Re-estimation of h2 after scaling the data with the correction factors was explored to obtain a measure of the improvement in the genetic evaluation and to detect changes in ranking of individuals and herds. Initial h2 estimates for EMA, P8, RIB12, and WEIGHT were .36, .39, .29, and .48, respectively. Scaling factors ranged from .25 for P8 in a herd with eight records to 1.96 for RIB12 in a herd with 86 individuals. Re-estimates of h2 increased by an average of 4.2% for all the traits as a result of correcting for heterogeneity. Deviations of new scaling factors were within expectations. Correlations between EBV with and without heterogeneity correction were greater than .97 for all the traits. However, some substantial re-rankings of herds were observed for some traits in the smaller herds.

Analysis of Variance↗

PubMed1995

Multiple-trait genetic evaluation for one polychotomous trait and several continuous traits with missing data and unequal models.

A method for multiple-trait genetic evaluation for categorical and continuous traits was generalized to a polychotomous rather than a binary trait and to several continuous traits rather than one. Any missing data pattern was allowed. Breeding values were estimated based on an animal model with fixed and random effects differing among traits. Equations in location parameters were solved iteratively within each Fisher scoring step. In each round of scoring, new solutions of the residual covariances among the categorical and the continuous traits were computed based on maximum likelihood estimation and used to reevaluate all partial regression coefficients of liability on the continuous traits for each missing data pattern. Simulation was used to assess the estimation of the residual covariances. The other dispersion parameters were treated as known because their estimation has been treated elsewhere and is analogous to restricted maximum likelihood.

Animals↗

PubMed1994

Use of multiple genetic markers in prediction of breeding values.

Genotypes at a marker locus give information on transmission of genes from parents to offspring and that information can be used in predicting the individuals' additive genetic value at a linked quantitative trait locus (MQTL). In this paper a recursive method is presented to build the gametic relationship matrix for an autosomal MQTL which requires knowledge on recombination rate between the marker locus and the MQTL linked to it. A method is also presented to obtain the inverse of the gametic relationship matrix. This information can be used in a mixed linear model for simultaneous evaluation of fixed effects, gametic effects at the MQTL and additive genetic effects due to quantitative trait loci unlinked to the marker locus (polygenes). An equivalent model can be written at the animal level using the numerator relationship matrix for the MQTL and a method for obtaining the inverse of this matrix is presented. Information on several unlinked marker loci, each of them linked to a different locus affecting the trait of interest, can be used by including an effect for each MQTL. The number of equations per animal in this case is 2m + 1 where m is the number of MQTL. A method is presented to reduce the number of equations per animal to one by combining information on all MQTL and polygenes into one numerator relationship matrix. It is illustrated how the method can accommodate individuals with partial or no marker information. Numerical examples are given to illustrate the methods presented. Opportunities to use the presented model in constructing genetic maps are discussed.

Animals↗

PubMed1992

Sire x herd interactions for weaning weight in beef cattle.

Weaning weight records of 44,357 Australian Angus calves produced by 1,020 sires in 90 herds were used to evaluate the importance of sire x herd interactions. Models fitted fixed effects of contemporary group (herd-year-date of weighing subclass), sex, calf age, and dam age and random effects of sire or of sire and sire x herd interaction using REML. Effects of standardizing the data, including sire relationships and including dam maternal breeding values (MBV) as a covariate were also investigated. Sire x herd interactions were found (P less than .05) in all cases and, in the most complete model, accounted for 3.3% of phenotypic variance. Across-herd heritabilities ranged from .19 to .28. Differential nonrandom mating among herds seemed to occur in the data. Significant sire x herd effects were observed for dam MBV, and adjustment for dam MBV yielded the smallest estimates of interaction variance and across-herd heritability. If sire x herd interactions were due only to genotype x environment interaction, within-herd heritabilities would range from .33 to .49. These estimates are larger than previously reported estimates. Thus, unreported environmental effects common to progeny of individual sires may also be involved in the observed interaction but could not be disentangled from true genotype x environment interaction effects using these data. Results of these analyses suggest that some accommodation of sire x herd interaction effects on weaning weight may be needed in beef cattle genetic evaluations, but a compelling case for development of herd-specific breeding value prediction cannot be made.

Animals↗

PubMedDate not supplied

A sampling algorithm for segregation analysis.

Methods for detecting Quantitative Trait Loci (QTL) without markers have generally used iterative peeling algorithms for determining genotype probabilities. These algorithms have considerable shortcomings in complex pedigrees. A Monte Carlo Markov chain (MCMC) method which samples the pedigree of the whole population jointly is described. Simultaneous sampling of the pedigree was achieved by sampling descent graphs using the Metropolis-Hastings algorithm. A descent graph describes the inheritance state of each allele and provides pedigrees guaranteed to be consistent with Mendelian sampling. Sampling descent graphs overcomes most, if not all, of the limitations incurred by iterative peeling algorithms. The algorithm was able to find the QTL in most of the simulated populations. However, when the QTL was not modeled or found then its effect was ascribed to the polygenic component. No QTL were detected when they were not simulated.

Algorithms↗