Biomedical subjects

Jack C M Dekkers

Publications and source records attributed to Jack C M Dekkers.

14 recordsLinked to original sources

PubMed2007

Associations of myostatin gene polymorphisms with performance and mortality traits in broiler chickens.

Myostatin is a negative regulator of skeletal muscle growth. We evaluated effects of myostatin polymorphisms in three elite commercial broiler chicken lines on mortality, growth, feed conversion efficiency, ultrasound breast depth, breast percentage, eviscerated carcass weight, leg defects, blood oxygen level, and hen antibody titer to infectious bursal disease virus vaccine. Progeny mean data adjusted for fixed and mate effects and DNA from 100 sires per line were used. Single nucleotide polymorphisms (SNPs) of the myostatin gene segregating in these lines were identified by designing specific primers, amplifying individual DNA in each line by polymerase chain reaction, cloning, sequencing and aligning the corresponding products. Individual sires were genotyped for five identified SNPs which contributed to eight haplotypes. Frequencies of SNP alleles and haplotypes differed between lines. Using the allele substitution effect model, the myostatin SNPs were found to have significant (P < 0.031) associations with growth, mortality, blood oxygen and hen antibody titer to infectious bursal disease virus vaccine, although the associations were not often consistent across lines. These results suggest that the myostatin gene has pleiotropic effects on broiler performance.

Animals↗

PubMed2006

Comparison of methods for analysis of selective genotyping survival data.

Survival traits and selective genotyping datasets are typically not normally distributed, thus common models used to identify QTL may not be statistically appropriate for their analysis. The objective of the present study was to compare models for identification of QTL associated with survival traits, in particular when combined with selective genotyping. Data were simulated to model the survival distribution of a population of chickens challenged with Marek disease virus. Cox proportional hazards (CPH), linear regression (LR), and Weibull models were compared for their appropriateness to analyze the data, ability to identify associations of marker alleles with survival, and estimation of effects when all individuals were genotyped (full genotyping) and when selective genotyping was used. Little difference in power was found between the CPH and the LR model for low censoring cases for both full and selective genotyping. The simulated data were not transformed to follow a Weibull distribution and, as a result, the Weibull model generally resulted in less power than the other two models and overestimated effects. Effect estimates from LR and CPH were unbiased when all individuals were genotyped, but overestimated when selective genotyping was used. Thus, LR is preferred for analyzing survival data when the amount of censoring is low because of ease of implementation and interpretation. Including phenotypic data of non-genotyped individuals in selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate, and therefore the CPH model is preferred for this scenario, although transformation of the data may also make the Weibull model appropriate for this case. The results from the research presented herein are directly applicable to interval mapping analyses.

Genotype↗

PubMed2005

Combined line-cross and half-sib QTL analysis of crosses between outbred lines.

Data from an F 2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0.6. The combined and half-sib models had similar power for FD<0.4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0.5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.

Animals↗

PubMed2004

Polar overdominant inheritance of a DLK1 polymorphism is associated with growth and fatness in pigs.

The polar overdominance model of inheritance was proposed to explain the non-Mendelian expression of callipyge muscular hypertrophy in sheep. The callipyge locus (CLPG) maps to the distal portion of ovine Chromosome 18 within the DLK1- GTL2 region and corresponds to human Chromosome 14q32, where uniparental disomy (UPD) of the region is associated with multiple congenital anomalies, including growth retardation and obesity. We investigated the porcine DLK1- GTL2 region in a cross of two pig breeds to determine if the callipyge polar overdominance is present in another species. Analyses of the parental origin of DLK1 polymorphism in the F2 offspring found that paternal inheritance of DLK1 allele 2 and maternal inheritance of the allele 1 was significantly associated with decreased fat deposition and increased lean muscle mass, while the opposite parental inheritance of these alleles was associated with slower prenatal and postnatal growth. These results suggest that the polar overdominance mode of inheritance is present in the pig chromosomal region that is homologous to the CLPG locus in sheep. Further study in pigs can provide important insights into understanding the molecular regulation of imprinted genes that are associated with human UPD14 and sheep callipyge phenotypes.

Alleles↗

PubMed2004

Investigation of obesity candidate genes on porcine fat deposition quantitative trait loci regions.

OBJECTIVES: To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity-related phenotypes. RESEARCH METHODS AND PROCEDURES: Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. RESULTS: Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene-gene interactions were also studied. DISCUSSION: Comparative analysis of obesity-related genes in the pig is not only important for development of marker-assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.

Adipose Tissue↗

PubMed2003

Tests of candidate genes in breed cross populations for QTL mapping in livestock.

In recent years, several F(2) crosses between outbred lines of livestock have been developed to identify quantitative trait loci (QTL). These populations are valuable for further genetic analysis, including positional candidate gene loci (CGL). Analysis of CGL in F(2) populations is, however, hindered by extensive between-breed linkage disequilibrium (LD). The objectives here were to develop and evaluate three tests for CGL in simulated F(2) breed-cross populations. 1) A standard association test, based on the fixed effect of CGL genotype. This test was significant for CGL at considerable distances from the QTL. 2) A marker-assisted association test, based on a test at the CGL of the fixed effect of CGL genotype in a breed-cross QTL interval mapping model. This removed the impact of between-breed LD, but was not powerful in detecting CGL closely linked to the QTL, unless the CGL was the QTL. 3) An F-drop test, comparing F ratios for a QTL at the CGL with and without the CGL included as fixed effect. It had low power to distinguish close from distant CGL. Power to distinguish two CGL within 10 cM from the QTL was limited and little improved by including QTL effects associated with markers to remove between-breed LD, although the power was greater when one of the CGL was the causative mutation. Therefore, while we conclude that candidate gene tests in QTL mapping populations must be interpreted with caution, we now have a clearer picture of the value of candidate gene tests in these populations.

Animals↗

PubMed2002

The use of molecular genetics in the improvement of agricultural populations.

Substantial advances have been made in the genetic improvement of agriculturally important animal and plant populations through artificial selection on quantitative traits. Most of this selection has been on the basis of observable phenotype, without knowledge of the genetic architecture of the selected characteristics. However, continuing molecular genetic analysis of traits in animal and plant populations is leading to a better understanding of quantitative trait genetics. The genes and genetic markers that are being discovered can be used to enhance the genetic improvement of breeding stock through marker-assisted selection.

Animals↗

PubMed2002

Application of the false discovery rate to quantitative trait loci interval mapping with multiple traits.

Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genome-wise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

Animals↗

PubMedDate not supplied

A method to optimize selection on multiple identified quantitative trait loci.

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.

Animals↗

PubMedDate not supplied

Optimal selection on two quantitative trait loci with linkage.

A mathematical approach to optimize selection on multiple quantitative trait loci (QTL) and an estimate of residual polygenic effects was applied to selection on two linked or unlinked additive QTL. Strategies to maximize total or cumulative discounted response over ten generations were compared to standard QTL selection on the sum of breeding values for the QTL and an estimated breeding value for polygenes, and to phenotypic selection. Optimal selection resulted in greater response to selection than standard QTL or phenotypic selection. Tight linkage between the QTL (recombination rate 0.05) resulted in a slightly lower response for standard QTL and phenotypic selection but in a greater response for optimal selection. Optimal selection capitalized on linkage by emphasizing selection on favorable haplotypes. When the objective was to maximize total response after ten generations and QTL were unlinked, optimal selection increased QTL frequencies to fixation in a near linear manner. When starting frequencies were equal for the two QTL, equal emphasis was given to each QTL, regardless of the difference in effects of the QTL and regardless of the linkage, but the emphasis given to each of the two QTL was not additive. These results demonstrate the ability of optimal selection to capitalize on information on the complex genetic basis of quantitative traits that is forthcoming.

Animals↗

PubMedDate not supplied

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models.

An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate non-additive gene action in a finite locus model. Under such a model, the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean, conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling, and three Markov chain Monte Carlo (MCMC) methods--scalar Gibbs, blocking Gibbs, and a sampler that combines the Elston Stewart algorithm with iterative peeling (ESIP). The performance of these four methods was assessed using simulated data. For pedigrees with loops, iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also, computing time is exponentially related to the number of loci in the model. For MCMC methods, a linear relationship can be maintained by sampling genotypes one locus at a time. Out of the three MCMC methods considered, ESIP, performed the best while scalar Gibbs performed the worst.

Genotype↗

PubMedDate not supplied

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP.

Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15,000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.

Chromosome Mapping↗

PubMedDate not supplied

Optimizing purebred selection for crossbred performance using QTL with different degrees of dominance.

A method was developed to optimize simultaneous selection for a quantitative trait with a known QTL within a male and a female line to maximize crossbred performance from a two-way cross. Strategies to maximize cumulative discounted response in crossbred performance over ten generations were derived by optimizing weights in an index of a QTL and phenotype. Strategies were compared to selection on purebred phenotype. Extra responses were limited for QTL with additive and partial dominance effects, but substantial for QTL with over-dominance, for which optimal QTL selection resulted in differential selection in male and female lines to increase the frequency of heterozygotes and polygenic responses. For over-dominant QTL, maximization of crossbred performance one generation at a time resulted in similar responses as optimization across all generations and simultaneous optimal selection in a male and female line resulted in greater response than optimal selection within a single line without crossbreeding. Results show that strategic use of information on over-dominant QTL can enhance crossbred performance without crossbred testing.

Alleles↗

PubMedDate not supplied

A study on the minimum number of loci required for genetic evaluation using a finite locus model.

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.

Animals↗