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Simple, sensitive and robust chicken specific sexing assays, compliant with large scale analysis.

ABSTRACT: Chicken meat and eggs are important sources of food for the world population. The significant increase in food demand has pushed the food industry toward a rapid non-expensive production which in turn raises ethical issues. How chicken are cultivated and processed in food industry is no longer acceptable. Ethical and economical concerns emerging from chicken culling need to be solved in the near future. Indeed, in egg production industry, male chicken are killed at the age of 1-day post-hatching since they are not egg producers. A number of laboratory all over the world are looking for innovative non-invasive sexing methods to determine the sex of chicken in the early stages of the development before hatching. It will allow males' chicken elimination before the pain-feeling stages. In order to evaluate the efficiency of these methods, the scientific community need a reliable, easy to use and cost-effective in-ovo invasive sexing method. In this report, we developed two new invasive assays based on PCR and Q-PCR techniques respectively, which fulfil the above mentioned requirements. In the same line with other groups, we exploited the differences betweed males (ZZ) and females (ZW) chicken sexual chromosomes. We identified two genes, SWIM and Xho-I, on chromosome W and DMRT gene on chromosome Z allowing a clear discrimination between the two sexes using PCR and qPCR respectively. These two new genomic markers and their corresponding methods not only increase the accuracy but also reduce time and cost of the test compared to previously developed sexing methods. Depending on the technology available in the lab, one can choose between the two techniques requiring different machines and expertise.

SUBMITTER: He L

PROVIDER: S-EPMC6396912 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP has a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies.

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Simple, sensitive and robust chicken specific sexing assays, compliant with large scale analysis.

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