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Disclosure of affected breed without disclosure of major progenitors has been the usual practice in scientific papers reporting recessive heritable disorders of cattle. Before molecular genetics, carrier identity could not be used by breeders to control causal mutations because phenotypically normal heterozygotes among genetically related animals could not be detected other than by test mating. Accurate, low-cost DNA tests fundamentally changed this situation. Genomics can provide relief from the old problem of emerging recessive disorders in cattle breeding, but greater transparency of genotype data between breeders is necessary to fully exploit the opportunities for cost-efficient genetic disease control. Effective control of several recessive disorders has been demonstrated in Angus cattle, based entirely on voluntary DNA testing by breeders but mandatory public disclosure of test results and genotype probabilities for all registered animals. When a DNA test is available, major progenitors (particularly bulls from which semen has been distributed) should be identified and disclosed concurrently with the affected breed. As a minimum, whenever possible the closest common ancestors in the pedigrees of the parents of homozygous mutants should be disclosed after confirmation of carrier status. Progenitor disclosure in scientific publications should occur in cooperation with breed societies, which should have the opportunity to advise breeders and initiate management programs before scientific publication. Unless properly managed, genomic enhancement of animal selection using SNP markers may increase inbreeding, co-ancestry and emergence of recessive disorders. The information systems and genotype disclosure policies of some breed societies will be increasingly challenged, particularly with accelerating mutation discovery using next-generation sequencing. [ABSTRACT FROM AUTHOR]

A good statistical analysis of genotype x environment interactions (G x E) is a key requirement for progress in any breeding program. Data for G x E analyses traditionally come from multi-environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing GxE. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for GxE. Based on our experiences in teaching statistical methods to plant breeders, for specialized GxE courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G x E analyses is two-fold. On the one hand, we see the G x E problem as the building of predictive models for genotype-specific reaction norms. On the other hand, the GxE problem consists in the identification of suitable variance-covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear-bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G x E, we show that our preference for mixed linear-bilinear models for analyzing G x E is supported by recent trends in the types of methods for G x E analysis that are most frequently cited. [ABSTRACT FROM AUTHOR]