G. Moser

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

BackgroundGenomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle.MethodsGenotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls.ResultsFor both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy.All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time.ConclusionsThe four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.

Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

BackgroundAt the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).MethodsDense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.ResultsRR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.ConclusionsAccurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.

Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle

BackgroundWe investigated strategies and factors affecting accuracy of imputing genotypes from lower-density SNP panels (Illumina 3K, 7K, Affymetrix 15K and 25K, and evenly spaced subsets) up to one medium (Illumina 50K) and one high-density (Illumina 800K) SNP panel. We also evaluated the utility of imputed genotypes on the accuracy of genomic selection using Australian Holstein-Friesian cattle data from 2727 and 845 animals genotyped with 50K and 800K SNP chip, respectively. Animals were divided into reference and test sets (genotyped with higher and lower density SNP panels, respectively) for evaluating the accuracies of imputation. For the accuracy of genomic selection, a comparison of direct genetic values (DGV) was made by dividing the data into training and validation sets under a range of imputation scenarios.ResultsOf the three methods compared for imputation, IMPUTE2 outperformed Beagle and fastPhase for almost all scenarios. Higher SNP densities in the test animals, larger reference sets and higher relatedness between test and reference animals increased the accuracy of imputation. 50K specific genotypes were imputed with moderate allelic error rates from 15K (2.85%) and 25K (2.75%) genotypes. Using IMPUTE2, SNP genotypes up to 800K were imputed with low allelic error rate (0.79% genome-wide) from 50K genotypes, and with moderate error rate from 3K (4.78%) and 7K (2.00%) genotypes. The error rate of imputing up to 800K from 3K or 7K was further reduced when an additional middle tier of 50K genotypes was incorporated in a 3-tiered framework. Accuracies of DGV for five production traits using imputed 50K genotypes were close to those obtained with the actual 50K genotypes and higher compared to using 3K or 7K genotypes. The loss in accuracy of DGV was small when most of the training animals also had imputed (50K) genotypes. Additional gains in DGV accuracies were small when SNP densities increased from 50K to imputed 800K.ConclusionPopulation-based genotype imputation can be used to predict and combine genotypes from different low, medium and high-density SNP chips with a high level of accuracy. Imputing genotypes from low-density SNP panels to at least 50K SNP density increases the accuracy of genomic selection.

Molecular characterization of the porcine testis-specific phosphoglycerate kinase 2 (PGK2) gene and its association with male fertility.

We have isolated and characterized the porcine testis-specific phosphoglycerate kinase 2 (PGK2) gene, and 1665 bp of full-length PGK2 cDNA were also compiled using modified rapid amplification 5′-RACE and 3′-RACE information. The results of genomic and cDNA sequences of the porcine PGK2 gene demonstrated that it is a single-exon intronless gene with a complete open reading frame of 1251 bp encoding a PGK protein of 417 amino acids. Real-time quantitative PCR results showed that PGK2 mRNA was solely expressed in the testis. There was a lower amount of PGK2 expression in the testis of a 10-month-old herniated boar and a very small amount of PGK2 expression in the testis of an 8–week-old cryptorchid piglet compared to an adult boar. Two SNPs in the PGK2 gene (SNP-A: T427C; SNP-B: C914A) resulting in amino acid substitutions (SNP-A: Ser102–Pro102; SNP-B: Thr264–Lys264) were detected and genotyped among six pig breeds. The nucleotide C at SNP-A responsible for the amino acid exchange to proline could lead to the loss of a casein kinase II (CK2) phosphorylation site in the PGK2 peptide. Association analyses between PGK2 genotypes and several traits of sperm quantity and quality were performed. The results showed that SNP-B has a positive significant effect on semen volume in the breed Pietrain (p = 0.08), i.e., boars carrying genotype CC revealed an increased volume of 49 ml compared with boars having the genotype AA.

A meta-analytic assessment of a Thyroglobulin marker for marbling in beef cattle

A meta-analysis was undertaken reporting on the association between a polymorphism in the Thyroglobulin gene (TG5) and marbling in beef cattle. A Bayesian hierarchical model was adopted, with alternative representations assessed through sensitivity analysis. Based on the overall posterior means and posterior probabilities, there is substantial support for an additive association between the TG5 marker and marbling. The marker effect was also assessed across various breed groups, with each group displaying a high probability of positive association between the T allele and marbling. The WinBUGS program code used to simulate the model is included as an Appendix available online at http://www.edpsciences.org/gse.

Genotype–covariate interaction effects and the heritability of adult body mass index

Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype–covariate interaction effects at common SNP loci. We find evidence for genotype–age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10−18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype–environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10−5 and LRT = 30.80, P = 1.42 × 10−8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens

Two novel methods for genome wide selection (GWS) were examined for predicting the genetic merit of animals using SNP information alone. A panel of 1,546 dairy bulls with reliable EBVs was genotyped for 15,380 SNPs that spanned the whole bovine genome. Two complexity reduction methods were used, partial least squares (PLS) and regression using a genetic algorithm (GAR), to find optimal solutions of EBVs against SNP information. Extensive internal cross-validation was used tofind the best predictive models followed by external validation (without direct use of the pedigree or SNP location). Both PLS and GAR provided both accurate fit to the training data set for somatic cell count (SCC) (max r = 0.83) and fertility (max r = 0.88) and showed an accuracy of prediction of r = 0.47 for SCC, and r = 0.72 for fertility. This is the first empirical demonstration that genome wide selection can account for a very high proportion of additive genetic variation in fitness traits whilst exploiting only a small percentage of available SNP information, without use of pedigree or QTL mapping. PLS was computationally more efficient than GAR.

Calcium-release-channel genotypes in several pig populations-associations with halothane and CK reactions.

SUMMARY DNA of 2985 pigs from different sources were tested for variants of the calcium-release-channel (CRC) gene. Frequencies of the C allele, associated with stress resistance, were 0.0 for Belgian Landrace, 0.01 for Pietrain, 0.54 for German Landrace, 0.86 for German-Landrace sowline, 0.91 for Schwäbisch-Hällisches swine, 0.95 for European Wildboar, and 0.99 for Large White. All 50 Meishan individuals tested were C/C. In the two German Landrace populations more individuals with heterozygous genotypes were observed than had been expected. These results may indicate balanced allele frequencies caused by overdominance-type selection associated with meat quantity. 6.0 % of the halothane-positive pigs were C/C or C/T, and 3.6 % of the halothane-negative animals were T/T. As some of the pig groups were crossbreeds from extremely divergent sources (e.g. European Wildboar, Meishan, Pietrain), special gene effects may have influenced the phenotypic reaction to halothane. The average CK values vary between pigs of different CRC genotypes, e.g., the CK(80) values 2.64 ± 0.023, 2.83 ± 0.027, and 3.19 ± 0.036 were measured for individuals of C/C, C/T and T/T, respectively. For the German Landrace, culling according to a threshold of CK(80) ≥ 2.70 would eliminate 29.1 % of C/C, 63.0 % of C/T, and 90.4 % of T/T individuals. Whether CK-based selection may be used for further selection in populations with a fixed CRC C allele is discussed. ZUSAMMENFASSUNG: Genotypen des Kalziumfreisetzungskanals in verschiedenen Schweinepopulationen-Zusammenhänge mit Halothan- und CK-Reaktionen Auf die Genvariante des Calciumfreisetzungskanales (CRC), die als Ursache für das Maligne Hyperthermic Syndrom beim Schwein angesehen wird, wurden 2985 Schweine verschiedener Herkünfte untersucht. Dabei ergaben sich folgende Allelfrequenzen für das C-Allel, welches in Zusammenhang mit der Streßresistenz steht: 0,0 bei der Belgischen Landrasse, 0,01 bei der Rasse Pietrain, 0,54 bei der Deutschen Landrasse, 0,86 bei der Deutschen Landrasse Sauenlinie, 0,91 beim Schwäbisch-Hällischen Schwein, 0,95 beim europäischen Wildschweine und 0,99 bei der Rasse Large White. Alle 50 untersuchten Meishan-Tiere zeigten den Genotyp C/C. Für die beiden untersuchten Populationen der Deutschen Landrasse wurden mehr heterozygote Genotypen beobachtet als erwartet waren. Dieses Ergebnis mag auf balancierte Allelfrequenzen hinweisen, die auf Überdominanzeffekten beruhen können. Insgesamt 6 % der im Halothan-Test als positiv eingestuften Schweine zeigten die Genotypen C/C bzw. C/T, während 3,6 % der Nicht-Reagenten den Genotyp T/T aufwiesen. Da manche der untersuchten Schweine Kreuzungsprodukte genetisch extrem unterschiedlicher Herkünfte sind (europäisches Wildschein, Meishan und Pietrain), wird vermutet, daß bestimmte Geneffekte die phänotypische Reaktion auf Halothan ausgelöst haben. Der CK(80) Wert unterschied sich bei den Schweinen unterschiedlicher CRC-Genotypen: 2,64 ± 0,023, 2,83 ± 0,027 und 3,19 ± 0,036 für die Tiere mit den Genotypen C/C, C/T bzw. T/T. Bei einer Selektionsschranke für den CK(80) von ≥ 2,70 müßten 29,1 % der C/C-, 63,0 % der C/T- aber lediglich 90,4 % der T/T-Tiere ausgeschlossen werden. Es wird diskutiert, wie eine Selektion nach dem CK-Wert in Populationen, in denen das C-Allel fixiert ist, wirkt.