Comparison of granddaughter design and general pedigree design analysis of QTL in dairy cattle: a simulation study

Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and locating QTL in a simulated complex pedigree. QTL analyses were performed under the variance component model containing a random QTL and a random polygenic effect. The covariance matrix of the polygenic effect was a standard additive relationship matrix. The (co)variance matrix of the random QTL effect contained probabilities that QTL alleles shared by two individuals were identical by descent (IBD). In the GDD analysis, IBD probabilities were calculated using sires’ and daughters’ marker genotypes. In the GPD analysis, IBD probabilities were obtained using a deterministic approach. The estimation of QTL position and variance components was conducted using REML algorithm. Although both methods were able to locate the region of the QTL properly, the GPD method showed better precision of QTL position estimates in most cases and significantly higher power than the GDD method.  

Linear and generalized linear models for the detection of QTL effects on within-subject variability

SummaryQuantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H0: No QTL with effect on the within-litter variance is segregating versus HA: There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests.

Determination of Allele Frequencies for Quantitative Trait Loci in Commercial Animal Populations

Individual loci affecting economic traits in dairy cattle (ETL) have been detected via linkage to genetic markers by application of the granddaughter design in the US population and the daughter design in the Israeli population. From these analyses it is not possible to determine allelic frequencies in the population at large, or whether the same alleles are segregating in different families. We proposed to answer this question by application of the "modified granddaughter design", in which granddaughters with a common maternal grandsire are both genotyped and analyzed for the economic traits. The objectives of the proposal were: 1) to fine map three segregating ETL previously detected by a daughter design analysis of the Israeli dairy cattle population; 2) to determine the effects of ETL alleles in different families relative to the population mean; 3) for each ETL, to determine the number of alleles and allele frequencies. The ETL on Bostaurusautosome (BT A) 6 chiefly affecting protein concentration was localized to a 4 cM chromosomal segment centered on the microsatellite BM143 by the daughter design. The modified granddaughter design was applied to a single family. The frequency of the allele increasing protein percent was estimated at 0.63+0.06. The hypothesis of equal allelic frequencies was rejected at p<0.05. Segregation of this ETL in the Israeli population was confirmed. The genes IBSP, SPP1, and LAP3 located adjacent to BM143 in the whole genome cattle- human comparative map were used as anchors for the human genome sequence and bovine BAC clones. Fifteen genes within 2 cM upstream of BM143 were located in the orthologous syntenic groups on HSA4q22 and HSA4p15. Only a single gene, SLIT2, was located within 2 cM downstream of BM143 in the orthologous HSA4p15 region. The order of these genes, as derived from physical mapping of BAC end sequences, was identical to the order within the orthologous syntenic groups on HSA4: FAM13A1, HERC3. CEB1, FLJ20637, PP2C-like, ABCG2, PKD2. SPP, MEP, IBSP, LAP3, EG1. KIAA1276, HCAPG, MLR1, BM143, and SLIT2. Four hundred and twenty AI bulls with genetic evaluations were genotyped for 12 SNPs identified in 10 of these genes, and for BM143. Seven SNPs displayed highly significant linkage disequilibrium effects on protein percentage (P<0.000l) with the greatest effect for SPP1. None of SNP genotypes for two sires heterozygous for the ETL, and six sires homozygous for the ETL completely corresponded to the causative mutation. The expression of SPP 1 and ABCG2 in the mammary gland corresponded to the lactation curve, as determined by microarray and QPCR assays, but not in the liver. Anti-sense SPP1 transgenic mice displayed abnormal mammary gland differentiation and milk secretion. Thus SPP 1 is a prime candidate gene for this ETL. We confirmed that DGAT1 is the ETL segregating on BTA 14 that chiefly effects fat concentration, and that the polymorphism is due to a missense mutation in an exon. Four hundred Israeli Holstein bulls were genotyped for this polymorphism, and the change in allelic frequency over the last 20 years was monitored.

Use of milk samples from a milk evaluation program for the genotyping of cows

Milk can be an attractive DNA-resource for genotyping milking cows, e.g. for paternity control or QTL analysis within a daughter design. The use of milk collected within the established milk evaluation programs enables the collection of large numbers of samples. Nevertheless, there are limitations when using the remedies of tested milk samples, e.g. permutations of samples or partially degraded DNA. A DNA preparation method suitable for samples from routine milk recording has been developed that combines an initial centrifugation step with direct lysis of cells and purification from comparatively high volumes by using silica membrane spin columns. The method yields high quality genomic DNA from fresh samples and PCR grade DNA from remedies of tested samples. In addition the potential use of milk samples within a daughter design was evaluated. We collected reference samples from 119 Simmental dairy cows from 6 half sib families. From 89 of these dairy cows remedies of milk samples were obtained from the routine milk laboratory. Paternity could be established by microsatellite analyses for all 119 reference cases. From the 89 milk laboratory samples 86 were successfully microsatellite typed. In 81 cases the genotypes from milk and reference sample were identical. In summary it could be shown that it is possible to genotype dairy cows from test laboratory milk samples, but results have to be used carefully taking into account inherent limitations. The use of milk as compared with tissue samples as a source for DNA within daughter designs is discussed.

Mapping Quantitative Trait Loci in F2 Incorporating Phenotypes of F3 Progeny

In plants and laboratory animals, QTL mapping is commonly performed using F2 or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F2 individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F3 progeny derived from selfing F2 plants in place of the F2 phenotype itself. All F3 progeny derived from the same F2 plant belong to the same F2:3 family, denoted by F2:3. If the size of each F2:3 family (the number of F3 progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F2 plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F2 marker genotypes and F3 average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F2:3 strategy. We also propose to combine both the F2 phenotypes and the F2:3 average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F2:3 families of heterozygous F2 plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F2 design, even if only a single F3 progeny is collected from each F2:3 family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.