Previously, we reported a genome wide association study (GWAS) that had shown association of a region between 11.8 and 13.6 Mbp on chromosome 9 with ascites phenotype in broilers. We had used microsatellite loci to demonstrate an association of particular genotypes for this region with ascites in experimental ascites lines and commercial broiler breeder lines. We identified two potential candidate genes, AGTR1 and UTS2D, within that chromosomal region for mediating the quantitative effect. We have now extended our analysis using SNPs for these genes to assess association with resistance or susceptibility to ascites in these same broiler lines. Surprisingly, in contrast to our previous GWAS and microsatellite data for this region, we find no association of the SNP genotypes or haplotypes in the region suggesting that the two genes might have limited association with the disease phenotype.

A quantitative trait locus on chromosome 9 was previously shown to be associated with ascites in multiple experimental and commercial populations. A study to evaluate the association of the QTL, based on variable number tandem repeat genotypes, with economically important traits was carried out on a commercial male elite line. Results indicated the highest fat and the lowest fillet mean were associated with the most resistant ascites genotype. All other traits measured for this genotype showed no trend towards positive or negatively impacting production values. The results suggest that a balanced approach could be undertaken in commercial broiler breeding operations to reduce ascites susceptibility in broiler populations without compromising overall genetic progress for traits of economic importance.

Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services. However, existing systems either forgo human raters entirely, thus harming the reliability of the test, or score every response by both human and machine thereby increasing costs. We target the spectrum of possible solutions in between, making use of both humans and machines to provide a higher quality test while keeping costs reasonable to democratize access to AS. In this work, we propose a combination of the existing paradigms, sampling responses to be scored by humans intelligently. We propose reward sampling and observe significant gains in accuracy (19.80% increase on average) and quadratic weighted kappa (QWK) (25.60% on average) with a relatively small human budget (30% samples) using our proposed sampling. The accuracy increase observed using standard random and importance sampling baselines are 8.6% and 12.2% respectively. Furthermore, we demonstrate the system's model agnostic nature by measuring its performance on a variety of models currently deployed in an AS setting as well as pseudo models. Finally, we propose an algorithm to estimate the accuracy/QWK with statistical guarantees (Our code is available at https://git.io/J1IOy).

No abstract