Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

A deterministic simulation study of embryo marker-assisted selection for age at first calving in Nellore (Bos indicus) beef cattle

We used deterministic simulation of four alternative multiple ovulation and embryo manipulation (MOET) closed nucleus schemes to investigate the benefits of using marker-assisted selection (MAS) of Nellore (Bos indicus) beef cattle embryos prior to transplantation to reduce the age at first calving (AFC). We found that MAS resulted in increased genetic gain as compared to selection without AFC quantitative trait loci (AFC-QTL) information. With single-stage selection the genetic response (GR) increased as follows: GR = 0.68% when the AFC-QTL explained 0.02 of the AFC additive genetic variance (sigma2A); GR = 1.76% for AFC-QTL explaining 0.05 sigma2A; GR = 3.7% for AFC-QTL explaining 0.1 sigma2A; and GR = 55.76% for AFC-QTL explaining 0.95 sigma2A. At the same total selected proportion, two-stage selection resulted in less genetic gain than single stage MAS at two-years of age. A single stage selection responses of > 95% occurred with pre-selected proportions of 0.4 (0.1 sigma2A explained by AFC-QTL), 0.2 (0.3 sigma2A explained by AFC-QTL) and 0.1 (0.5 sigma2A explained by AFC-QTL), indicating that the combined use of MAS and pre-selection can substantially reduce the cost of keeping recipient heifers in MOET breeding schemes. When the number of recipients was kept constant, the benefit of increasing embryo production was greater for the QTL explaining a higher proportion of the additive genetic variance. However this advantage had a diminishing return especially for QTL explaining a small proportion of the additive genetic variance. Thus, marker assisted selection of embryos can be used to achieve increased genetic gain or a similar genetic response at reduced expense by decreasing the number of recipient cows and number of offspring raised to two-years of age