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Biomedical subjects

B Guldbrandtsen

Publications and source records attributed to B Guldbrandtsen.

7 recordsLinked to original sources
PubMed2007

Detection of quantitative trait loci affecting lameness and leg conformation traits in Danish Holstein cattle.

Lameness is an important factor for culling animals. Strong legs and feet improve herd life of dairy cows. Therefore, many countries include leg and feet conformation traits in their breeding programs, often as early predictors of longevity. However, few countries directly measure lameness related traits to include these in a breeding program. Lameness indices in 3 different lactations and 5 leg conformation traits (rear legs side view, rear legs rear view, hock quality, bone quality, and foot angle) were measured on granddaughters of 19 Danish Holstein grandsires with 33 to 105 sons. A genome scan was performed to detect quantitative trait loci (QTL) based on the 29 autosomes using microsatellite markers. Data were analyzed across and within families for QTL affecting lameness and leg conformation traits. A regression method and a variance component method were used for QTL detection. Two QTL each for lameness in the first [Bos taurus autosome (BTA); BTA5, BTA26] and second (BTA19, BTA22) lactations were detected. For the 5 different leg conformation traits, 7 chromosome-wise significant QTL were detected across families for rear legs side view, 5 for rear legs rear view, 4 for hock quality, 4 for bone quality, and 1 for foot angle. For those chromosomes where a QTL associated with 2 different traits was detected (BTA1, BTA11, BTA15, BTA26, and BTA27), a multitrait-1-QTL model and a multitrait-2-QTL model were performed to characterize these QTL as single QTL with pleiotropic effects or distinct QTL.

Animals↗
PubMed2006

Multitrait quantitative trait Loci mapping for milk production traits in danish Holstein cattle.

The aims of this study were (1) to confirm previously identified quantitative trait loci (QTL) on bovine chromosomes 6, 11, 14, and 23 in the Danish Holstein cattle population, (2) to assess the pleiotropic nature of each QTL on milk production traits by building multitrait and multi-QTL models, and (3) to include pedigree information on nongenotyped individuals to improve the estimation of genetic parameters underlying the random QTL model. Nineteen grandsire families were analyzed by single-trait (ST) and multitrait (MT) QTL mapping methods. The variance component-based QTL mapping model was implemented via restricted maximum likelihood (REML) to estimate QTL position and parameters. Segregation of the previously identified QTL was confirmed on bovine chromosomes 6, 11, and 14, but not on 23. A highly significant (1% chromosome-wise level) QTL was found on chromosome 6, between 37 and 73 cM. This QTL had a strong effect on protein percentage (PP) and fat percentage (FP) according to ST analyses, and effects on PP, FP, milk yield (MY), fat yield (FY), and protein yield (PY) in MT analyses. A QTL affecting PP was detected on chromosome 11 (at 70 cM) using ST analysis. The MT analysis revealed a second QTL (at 67 cM) approaching significance with an effect on MY. The ST analysis identified a QTL for MY and FP on chromosome 14, between 10 and 24 cM. The extended pedigree (nongenotyped animals) was included to estimate genetic parameters underlying the random QTL model; that is, additive polygenic and QTL variances. In general, the estimates of the QTL variance components were smaller but more precise when the extended pedigree was considered in the analysis.

Animals↗
PubMed2003

Multitrait fine mapping of quantitative trait loci using combined linkage disequilibria and linkage analysis.

A novel multitrait fine-mapping method is presented. The method is implemented by a model that treats QTL effects as random variables. The covariance matrix of allelic effects is proportional to the IBD matrix, where each element is the probability that a pair of alleles is identical by descent, given marker information and QTL position. These probabilities are calculated on the basis of similarities of marker haplotypes of individuals of the first generation of genotyped individuals, using "gene dropping" (linkage disequilibrium) and transmission of markers from genotyped parents to genotyped offspring (linkage). A small simulation study based on a granddaughter design was carried out to illustrate that the method provides accurate estimates of QTL position. Results from the simulation also indicate that it is possible to distinguish between a model postulating one pleiotropic QTL affecting two traits vs. one postulating two closely linked loci, each affecting one of the traits.

Chromosome Mapping↗
PubMed2000

A polymorphic major histocompatibility complex class II-like locus maps outside of both the chicken B-system and Rfp-Y-system.

Chickens have two major regions encoding major histocompatibility complex (MHC) class Ialpha genes and MHC class IIss genes, the serological and functional B-system and the Rfp-Y-system. Recently, they have been shown to assort in a genetically independent way although still located on the same microchromosome. Moreover, the monomorphic MHC class IIalpha gene maps at a third locus located 5 cM from the nearest class IIss genes, located in the B-system (Kaufman et al., 1995). A pedigree family was studied in three generations in order to assign MHC class IIss restriction fragments observed in Southern blot analyses to either the B-system, the Rfp-Y-system or the B-Lalpha locus. In this study, we demonstrate by classical genetic testing of chickens within this fully pedigreed family the existence of an MHC class II-like polymorphic restriction fragment that segregates independently of the B-system, the Rfp-Y-system and of the B-Lalpha locus.

Alleles↗
PubMed1998

Population differentiation through mutation and drift--a comparison of genetic identity measures.

Different genetic identity or distance measures are compared that consider allelic variation within and between populations. Particularily we analyse those suggested by Nei (IS, DS), Rogers (DR), Reynolds, Weir and Cockerham (D theta), Nei, Tajima and Tateno (DA), Tomiuk and Loeschcke (ITL, DTL) and Goldstein et al. ((delta mu)2). The simulations focus on the influence of non-equilibrium conditions on the stability of these measures. The degree of homozygosity of an ancestral population before it splits into two sister populations is most important for the stability of the different estimates of genetic identity. If populations are not close to their equilibrium homozygosity, a considerable bias can occur and, thereby, provide very misleading estimates of the time span since divergence. The ITL-measure based on estimates of ancestral alleles is more robust than other measures of genetic identity, especially for large population sizes and high mutation rates. For the infinite allele model, the analysis shows that more precise estimates of the frequency of ancestral alleles can greatly improve the reliability of the estimate of genetic identity in the case of ITL. For the stepwise mutation model, the TL-measure combines the attributes of the DA- and (delta mu)2-measures. The TL-measure is efficient for the construction of the correct tree topology of related populations as well as for the estimation of the branch length when protein or microsatellite data are analysed.

Alleles↗
PubMedDate not supplied

Sampling genotypes in large pedigrees with loops.

Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Scalar-Gibbs is easy to implement, and it is widely used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This paper proposes a method to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling, and is called the ESIP sampler. For a hypothetical pedigree, genotype probabilities are estimated from samples obtained using ESIP and also scalar-Gibbs. Approximate probabilities were also obtained by iterative peeling. Comparisons of these with exact genotypic probabilities obtained by the Elston-Stewart algorithm showed that ESIP and iterative peeling yielded genotypic probabilities that were very close to the exact values. Nevertheless, estimated probabilities from scalar-Gibbs with a chain of length 235 000, including a burn-in of 200 000 steps, were less accurate than probabilities estimated using ESIP with a chain of length 10 000, with a burn-in of 5 000 steps. The effective chain size (ECS) was estimated from the last 25 000 elements of the chain of length 125 000. For one of the ESIP samplers, the ECS ranged from 21 579 to 22 741, while for the scalar-Gibbs sampler, the ECS ranged from 64 to 671. Genotype probabilities were also estimated for a large real pedigree consisting of 3 223 individuals. For this pedigree, it is not feasible to obtain exact genotype probabilities by the Elston-Stewart algorithm. ESIP and iterative peeling yielded very similar results. However, results from scalar-Gibbs were less accurate.

Algorithms↗