Marie-Noëlle Fouilloux

High-density marker imputation accuracy in sixteen French cattle breeds

BackgroundGenotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.MethodsFive thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.ResultsMean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.ConclusionIn all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

Novel Insights into the Bovine Polled Phenotype and Horn Ontogenesis in Bovidae

Despite massive research efforts, the molecular etiology of bovine polledness and the developmental pathways involved in horn ontogenesis are still poorly understood. In a recent article, we provided evidence for the existence of at least two different alleles at the Polled locus and identified candidate mutations for each of them. None of these mutations was located in known coding or regulatory regions, thus adding to the complexity of understanding the molecular basis of polledness. We confirm previous results here and exhaustively identify the causative mutation for the Celtic allele (PC) and four candidate mutations for the Friesian allele (PF). We describe a previously unreported eyelash-and-eyelid phenotype associated with regular polledness, and present unique histological and gene expression data on bovine horn bud differentiation in fetuses affected by three different horn defect syndromes, as well as in wild-type controls. We propose the ectopic expression of a lincRNA in PC/p horn buds as a probable cause of horn bud agenesis. In addition, we provide evidence for an involvement of OLIG2, FOXL2 and RXFP2 in horn bud differentiation, and draw a first link between bovine, ovine and caprine Polled loci. Our results represent a first and important step in understanding the genetic pathways and key process involved in horn bud differentiation in Bovidae.

A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

A sampling-based method for estimating the accuracy of estimated breeding values using an animal model is presented. Empirical variances of true and estimated breeding values were estimated from a simulated n-sample. The method was validated using a small data set from the Parthenaise breed with the estimated coefficient of determination converging to the true values. It was applied to the French Salers data file used for the 2000 on-farm evaluation (IBOVAL) of muscle development score. A drawback of the method is its computational demand. Consequently, convergence can not be achieved in a reasonable time for very large data files. Two advantages of the method are that a) it is applicable to any model (animal, sire, multivariate, maternal effects...) and b) it supplies off-diagonal coefficients of the inverse of the mixed model equations and can therefore be the basis of connectedness studies.

Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.

Genetic parameters of beef traits of Limousin and Charolais progeny-tested AI sires

Sire selection efficiency depends on the knowledge of accurate genetic parameters. In France, artificial insemination (AI) sires are selected according to their own performances and those of their progeny, which are both recorded in test stations. Genetic parameters among progeny traits were estimated using multi-trait REML (restricted estimation of maximum likelihood) analyses in Charolais and Limousin breeds. The expected decrease in genetic variability algebraically calculated among progeny traits due to the selection of sires was not observed. This selection was not a strict truncation. Heritabilities of traits measured on progeny are moderate for growth traits, morphology and live fatness scores (from 0.14 to 0.38) and slightly higher for dressing percentage and carcass fatness score (0.50 and 0.44, respectively). Genetic correlations among progeny traits depended on traits, selection programme and breed. Carcass weight and morphology were highly genetically linked to corresponding live traits (live weight and conformation, respectively). They can, therefore, be easily improved through indirect selection in contrast to carcass fatness which has only a small genetic correlation with live traits.

Alternative haplotype construction methods for genomic evaluation

Genomic evaluation methods today use single nucleotide polymorphism (SNP) as genomic markers to trace quantitative trait loci (QTL). Today most genomic prediction procedures use biallelic SNP markers. However, SNP can be combined into short, multiallelic haplotypes that can improve genomic prediction due to higher linkage disequilibrium between the haplotypes and the linked QTL. The aim of this study was to develop a method to identify the haplotypes, which can be expected to be superior in genomic evaluation, as compared with either SNP or other haplotypes of the same size. We first identified the SNP (termed as QTL-SNP) from the bovine 50K SNP chip that had the largest effect on the analyzed trait. It was assumed that these SNP were not the causative mutations and they merely indicated the approximate location of the QTL. Haplotypes of 3, 4, or 5 SNP were selected from short genomic windows surrounding these markers to capture the effect of the QTL. Two methods described in this paper aim at selecting the most optimal haplotype for genomic evaluation. They assumed that if an allele has a high frequency, its allele effect can be accurately predicted. These methods were tested in a classical validation study using a dairy cattle population of 2,235 bulls with genotypes from the bovine 50K SNP chip and daughter yield deviations (DYD) on 5 dairy cattle production traits. Combining the SNP into haplotypes was beneficial with all tested haplotypes, leading to an average increase of 2% in terms of correlations between DYD and genomic breeding value estimates compared with the analysis when the same SNP were used individually. Compared with haplotypes built by merging the QTL-SNP with its flanking SNP, the haplotypes selected with the proposed criteria carried less under- and over-represented alleles: the proportion of alleles with frequencies <1 or >40% decreased, on average, by 17.4 and 43.4%, respectively. The correlations between DYD and genomic breeding value estimates increased by 0.7 to 0.9 percentage points when the haplotypes were selected using any of the proposed methods compared with using the haplotypes built from the QTL-SNP and its flanking markers. We showed that the efficiency of genomic prediction could be improved at no extra costs, only by selecting the proper markers or combinations of markers for genomic prediction. One of the presented approaches was implemented in the new genomic evaluation procedure applied in dairy cattle in France in April 2015.

Trends of the genetic connectedness measures among Nelore beef cattle herds

Validity of comparisons between expected breeding values obtained from best linear unbiased prediction procedures in genetic evaluations is dependent on genetic connectedness among herds. Different cattle breeding programmes have their own particular features that distinguish their database structure and can affect connectedness. Thus, the evolution of these programmes can also alter the connectedness measures. This study analysed the evolution of the genetic connectedness measures among Brazilian Nelore cattle herds from 1999 to 2008, using the French Criterion of Admission to the group of Connected Herds (CACO) method, based on coefficients of determination (CD) of contrasts. Genetic connectedness levels were analysed by using simple and multiple regression analyses on herd descriptors to understand their relationship and their temporal trends from the 1999-2003 to the 2004-2008 period. The results showed a high level of genetic connectedness, with CACO estimates higher than 0.4 for the majority of them. Evaluation of the last 5-year period showed only a small increase in average CACO measures compared with the first 5 years, from 0.77 to 0.80. The percentage of herds with CACO estimates lower than 0.7 decreased from 27.5% in the first period to 16.2% in the last one. The connectedness measures were correlated with percentage of progeny from connecting sires, and the artificial insemination spread among Brazilian herds in recent years. But changes in connectedness levels were shown to be more complex, and their complete explanation cannot consider only herd descriptors. They involve more comprehensive changes in the relationship matrix, which can be only fully expressed by the CD of contrasts.