R. J. C. Cantet

Reduced animal model for marker assisted selection using best linear unbiased prediction

A reduced animal model (RAM) version of the animal model (AM) incorporating independent marked quantitative trait loci (M(aTL’s) of Fernando and Grossman (1989) is presented. Both AM and RAM permit obtaining Best Linear Unbiased Predictions of MQTL effects plus the remaining portion of the breeding value that is not accounted for by independent M(aTL’s. RAM reduces computational requirements by

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

BackgroundF2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.ResultsSelection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.ConclusionsCombining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Theory for modelling means and covariances in a two-breed population with dominance inheritance.

This paper presents theory and methods to compute genotypic means and covariances in a two breed population under dominance inheritance, assuming multiple unlinked loci. It is shown that the genotypic mean is a linear function of five location parameters and that the genotypic covariance between relatives is a linear function of 25 dispersion parameters. Recursive procedures are given to compute the necessary identity coefficients. In the absence of inbreeding, the number of parameters for the mean is reduced from five to three and the number for the covariance is reduced from 25 to 12. In a two-breed population, for traits exhibiting dominance, the theory presented here can be used to obtain genetic evaluations by best linear unbiased prediction and to estimate genetic parameters by maximum likelihood.

Estimates of dispersion parameters and of genetic and environmental trends for weaning weight in Angus cattle using a maternal animal model with genetic grouping

Variance components for additive direct, additive maternal, maternal environmental and direct environmental effects, and the covariance between additive direct and maternal effects were estimated by restricted maximum likelihood from 935 weaning weights of Angus calves. An animal model including genetic grouping, with the same grouping criterion for direct and maternal effects, was employed. Estimates of (co)variance components in the model including genetic groups did not differ very much from those obtained when genetic groups were excluded. Estimates of heritability for direct and maternal effects, and of the correlation between direct and maternal effects (rG) were 0.11, 0.03 and −0.31, respectively. The estimated genetic trend for direct effects was positive whereas the estimated additive trend was close to zero. Estimates of environmental trends were positive for direct effects and null for maternal effects. The value of rG did not affect estimates of genetic trends for direct effects. The estimate of the genetic maternal trend was slightly sensitive to variation in the value and sign of rG and tended to increase as this correlation increased, but was not significantly different from zero for any of the values of rG considered.

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations

BackgroundCurrently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.ResultsThe standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance.ConclusionsThe standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.

Estimation of segregation variance for birth weight in beef cattle

Abstract Genetic evaluation using multibreed covariance theory requires estimating the segregation variance. The segregation variance is the amount by which the additive variance in the F2 exceeds that in F1. The goal of this research was to obtain REML estimates of the additive variances plus segregation variance, assuming equal environmental variances for all genetic groups. The data were originated in two experimental herds of beef cattle from New Zealand (NZ) and Argentina (AR). Records were birth weights of 4082 Angus–Hereford (NZ) and 6963 Nellore–Hereford (AR) cross calves, including purebreds, F1, backcrosses, F2, and advanced generations (F3, F4, F5). Variance components were estimated using an additive animal model by REML, with a first-derivative algorithm. The asymptotic standard errors of the REML estimates were calculated using the inverse of the information matrix. After 400 iterations, estimates of the additive variances (in kg2) were 7.77±0.91 (Angus) and 10.02±1.11 (Hereford), and estimate of the segregation variance was 1.14±0.85, in NZ data. Whereas in AR data, estimates of the additive variances were 6.59±0.71 (Nellore) and 8.97±0.75 (Hereford), and estimate of the segregation variance was 1.48±0.74. The error variances were estimated to be 7.92±0.06 in NZ and 6.86±0.06 in AR. Asymptotic tests (Likelihood Ratio and Lagrange Multiplier) of the hypothesis of null segregation variance suggested that this was not the case in these data.

Genetic grouping for direct and maternal effects with differential assignment of groups

Résumé La constitution de groupes génétiques avec affectation différentielle selon les effets directs et maternels. La constitution de groupes génétiques dans le cadre de modèles additifs avec effet maternel est généralisée à une affectation différentielle à des groupes selon les effets directs et maternels. Ce regroupement différentiel fournit une méthode pour inclure les groupes génétiques dans l’évaluation des animaux quand, par exemple, les progrès génétiques pour les effets directs et maternels sont différents. Cette généralisation est basée sur l’inclusion des mêmes animaux dans les 2 vecteurs d’effets additifs directs et maternels et sur l’exploitation des structures de Kronecker résultantes,

Refining genomewide association for growth and fat deposition traits in an F pig population.

The identification of genomic regions that affect additive genetic variation and contain genes involved in controlling growth and fat deposition has enormous impact in the farm animal industry (e.g., carcass merit and meat quality). Therefore, a genomewide association study was implemented in an F pig population using a 60,000 SNP marker panel for traits related to growth and fat deposition. Estimated genomic EBV were linearly transformed to calculate SNP effects and to identify genomic positions possibly associated with the genetic variability of each trait. Genomic segments were then defined considering the markers included in a region 1 Mb up- and downstream from the SNP with the smallest -value and a false discovery rate < 0.05 for each trait. The significance for each 2-Mb segment was tested using the Bonferroni correction. Significant SNP were detected on SSC2, SSC3, SSC5, and SSC6, but 2-Mb segment significant effects were observed on SSC3 for weight at birth (wt_birth) and on SSC6 for 10th-rib backfat and last-rib backfat measured by ultrasound at different ages. Furthermore, a 6-Mb segment on SSC6 was also considered because the 2-Mb segments for 10 different fat deposition traits were overlapped. Although the segment effects for each trait remain significant, the proportion of additive variance explained by this larger segment was slightly smaller in some traits. In general, the results confirm the presence of genetic variability for wt_birth on SSC3 (18.0-20.2 Mb) and for fat deposition traits on SSC6 (133.8-136.0 Mb). Within these regions, fibrosin () and myosin light chain, phosphorylatable, fast skeletal muscle () genes could be considered as candidates for the wt_birth signal on SSC3, and the SERPINE1 mRNAbinding protein 1 gene () may be a candidate for the fat deposition trait signals on SSC6.