Identification of Mendelian inconsistencies between SNP and pedigree information of sibs

Background Using SNP genotypes to apply genomic selection in breeding programs is becoming common practice. Tools to edit and check the quality of genotype data are required. Checking for Mendelian inconsistencies makes it possible to identify animals for which pedigree information and genotype information are not in agreement. Methods Straightforward tests to detect Mendelian inconsistencies exist that count the number of opposing homozygous marker (e.g. SNP) genotypes between parent and offspring (PAR-OFF). Here, we develop two tests to identify Mendelian inconsistencies between sibs. The first test counts SNP with opposing homozygous genotypes between sib pairs (SIBCOUNT). The second test compares pedigree and SNP-based relationships (SIBREL). All tests iteratively remove animals based on decreasing numbers of inconsistent parents and offspring or sibs. The PAR-OFF test, followed by either SIB test, was applied to a dataset comprising 2,078 genotyped cows and 211 genotyped sires. Theoretical expectations for distributions of test statistics of all three tests were calculated and compared to empirically derived values. Type I and II error rates were calculated after applying the tests to the edited data, while Mendelian inconsistencies were introduced by permuting pedigree against genotype data for various proportions of animals. Results Both SIB tests identified animal pairs for which pedigree and genomic relationships could be considered as inconsistent by visual inspection of a scatter plot of pairwise pedigree and SNP-based relationships. After removal of 235 animals with the PAR-OFF test, SIBCOUNT (SIBREL) identified 18 (22) additional inconsistent animals. Seventeen animals were identified by both methods. The numbers of incorrectly deleted animals (Type I error), were equally low for both methods, while the numbers of incorrectly non-deleted animals (Type II error), were considerably higher for SIBREL compared to SIBCOUNT. Conclusions Tests to remove Mendelian inconsistencies between sibs should be preceded by a test for parent-offspring inconsistencies. This parent-offspring test should not only consider parent-offspring pairs based on pedigree data, but also those based on SNP information. Both SIB tests could identify pairs of sibs with Mendelian inconsistencies. Based on type I and II error rates, counting opposing homozygotes between sibs (SIBCOUNT) appears slightly more precise than comparing genomic and pedigree relationships (SIBREL) to detect Mendelian inconsistencies between sib

Design of a Bovine Low-Density SNP Array Optimized for Imputation

The Illumina BovineLD BeadChip was designed to support imputation to higher density genotypes in dairy and beef breeds by including single-nucleotide polymorphisms (SNPs) that had a high minor allele frequency as well as uniform spacing across the genome except at the ends of the chromosome where densities were increased. The chip also includes SNPs on the Y chromosome and mitochondrial DNA loci that are useful for determining subspecies classification and certain paternal and maternal breed lineages. The total number of SNPs was 6,909. Accuracy of imputation to Illumina BovineSNP50 genotypes using the BovineLD chip was over 97% for most dairy and beef populations. The BovineLD imputations were about 3 percentage points more accurate than those from the Illumina GoldenGate Bovine3K BeadChip across multiple populations. The improvement was greatest when neither parent was genotyped. The minor allele frequencies were similar across taurine beef and dairy breeds as was the proportion of SNPs that were polymorphic. The new BovineLD chip should facilitate low-cost genomic selection in taurine beef and dairy cattle

Cubic-spline interpolation to estimate effects of inbreeding on milk yield in first lactation Holstein cows

Milk yield records (305d, 2X, actual milk yield) of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β0 + β1X + e), quadratic regression, (y = β0 + β1X + β2X2 + e) cubic regression (y = β0 + β1X + β2X2 + β3X3 +e) and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yield. Ten animal models, all with herd-year-season of calving as fixed effect, were compared using the Akaike corrected-Information Criterion (AICc). The cubic-spline interpolation model with seven knots had the lowest AICc, whereas for all those labeled as “traditional”, AICc was higher than the best model. Results from fitting inbreeding using a cubic-spline with seven knots were compared to results from fitting inbreeding as a linear covariate or as a fixed factor with seven levels. Estimates of inbreeding effects were not significantly different between the cubic-spline model and the fixed factor model, but were significantly different from the linear regression model. Milk yield decreased significantly at inbreeding levels greater than 9%. Variance component estimates were similar for the three models. Ranking of the top 100 sires with daughter records remained unaffected by the model used

Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows

<p>Abstract</p> <p>Background</p> <p>Genome-wide association analysis is a powerful tool for annotating phenotypic effects on the genome and knowledge of genes and chromosomal regions associated with dairy phenotypes is useful for genome and gene-based selection. Here, we report results of a genome-wide analysis of predicted transmitting ability (PTA) of 31 production, health, reproduction and body conformation traits in contemporary Holstein cows.</p> <p>Results</p> <p>Genome-wide association analysis identified a number of candidate genes and chromosome regions associated with 31 dairy traits in contemporary U.S. Holstein cows. Highly significant genes and chromosome regions include: BTA13's <it>GNAS </it>region for milk, fat and protein yields; BTA7's <it>INSR </it>region and BTAX's <it>LOC520057 </it>and <it>GRIA3 </it>for daughter pregnancy rate, somatic cell score and productive life; BTA2's <it>LRP1B </it>for somatic cell score; BTA14's <it>DGAT1-NIBP </it>region for fat percentage; <it>BTA1</it>'s <it>FKBP2 </it>for protein yields and percentage, BTA26's <it>MGMT </it>and BTA6's <it>PDGFRA </it>for protein percentage; BTA18's 53.9-58.7 Mb region for service-sire and daughter calving ease and service-sire stillbirth; BTA18's <it>PGLYRP1</it>-<it>IGFL1 </it>region for a large number of traits; BTA18's <it>LOC787057 </it>for service-sire stillbirth and daughter calving ease; BTA15's <it>CD82</it>, BTA23's <it>DST </it>and the <it>MOCS1</it>-<it>LRFN2 </it>region for daughter stillbirth; and BTAX's <it>LOC520057 </it>and <it>GRIA3 </it>for daughter pregnancy rate. For body conformation traits, BTA11, BTAX, BTA10, BTA5, and BTA26 had the largest concentrations of SNP effects, and <it>PHKA2 </it>of BTAX and <it>REN </it>of BTA16 had the most significant effects for body size traits. For body shape traits, BTAX, BTA19 and BTA3 were most significant. Udder traits were affected by BTA16, BTA22, BTAX, BTA2, BTA10, BTA11, BTA20, BTA22 and BTA25, teat traits were affected by BTA6, BTA7, BTA9, BTA16, BTA11, BTA26 and BTA17, and feet/legs traits were affected by BTA11, BTA13, BTA18, BTA20, and BTA26.</p> <p>Conclusions</p> <p>Genome-wide association analysis identified a number of genes and chromosome regions associated with 31 production, health, reproduction and body conformation traits in contemporary Holstein cows. The results provide useful information for annotating phenotypic effects on the dairy genome and for building consensus of dairy QTL effects.</p

Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle

<p>Abstract</p> <p>Background</p> <p>The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle.</p> <p>Methods</p> <p>Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (−maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models.</p> <p>Results and discussion</p> <p>On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (−maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (−maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence.</p> <p>Conclusions</p> <p>For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (−maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions.</p

Modeling relationships between calving traits: a comparison between standard and recursive mixed models

<p>Abstract</p> <p>Background</p> <p>The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype.</p> <p>Methods</p> <p>Four models were compared in terms of goodness of fit and predictive ability: 1) standard mixed model (SMM), a model with unstructured (co)variance matrices; 2) recursive mixed model 1 (RMM1), assuming that residual correlations are due to the recursive relationships between phenotypes; 3) RMM2, assuming that correlations between residuals and contemporary groups are due to recursive relationships between phenotypes; and 4) RMM3, postulating that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes.</p> <p>Results</p> <p>For all the RMM considered, the estimates of the structural coefficients were similar. Results revealed a nonlinear relationship between GL and the liabilities both to CD and to SB, and a linear relationship between the liabilities to CD and SB.</p> <p>Differences in terms of goodness of fit and predictive ability of the models considered were negligible, suggesting that RMM3 is plausible.</p> <p>Conclusions</p> <p>The applications examined in this study suggest the plausibility of a nonlinear recursive effect from GL onto CD and SB. Also, the fact that the most restrictive model RMM3, which assumes that the only cause of correlation is phenotypic recursion, performs as well as the others indicates that the phenotypic recursion may be an important cause of the observed patterns of genetic and environmental correlations.</p