A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep

Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as “cheese-making traits.” Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK20) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.

Genetic Analysis of Deep Phenotyping Projects in Common Disorders

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.