Integrating mRNA transcripts and genomic information into genomic prediction.

Genomic prediction has emerged as a pivotal technology for the genetic evaluation of livestock, crops, and for predicting human disease risks. However, classical genomic prediction methods face challenges in incorporating biological prior information such as the genetic regulation mechanisms of traits. This study introduces a novel approach that integrates mRNA transcript information to predict complex trait phenotypes. To evaluate the accuracy of the new method, we utilized a Drosophila population that is widely employed in quantitative genetics researches globally. Results indicate that integrating mRNA transcript data can significantly enhance the genomic prediction accuracy for certain traits, though it does not improve phenotype prediction accuracy for all traits. Compared with GBLUP, the prediction accuracy for olfactory response to dCarvone in male Drosophila increased from 0.256 to 0.274. Similarly, the accuracy for cafe in male Drosophila rose from 0.355 to 0.401. The prediction accuracy for survival_paraquat in male Drosophila is improved from 0.101 to 0.138. In female Drosophila, the accuracy of olfactory response to 1hexanol increased from 0.147 to 0.210. In conclusion, integrating mRNA transcripts can substantially improve genomic prediction accuracy of certain traits by up to 43%, with range of 7% to 43%. Furthermore, for some traits, considering interaction effects along with mRNA transcript integration can lead to even higher prediction accuracy.

A sensitive and specific PCR assay for the detection of Baylisascaris schroederi eggs in giant panda feces.

Baylisascaris schroederi is one of the most common intestinal nematodes in giant pandas. It can cause severe baylisascariasis which is highly infectious in its natural hosts. A rapid and reliable diagnosis of parasite infections is crucial to protect giant pandas, as well as for environmental monitoring and disease surveillance. Here, we established a specific PCR assay for B. schroederi detection which was targeting a 331-bp long fragment of the mitochondrial cytochrome c oxidase subunit II (COII) gene. Fifty fresh fecal samples collected from captive giant pandas were tested by the established PCR assay and the traditional flotation technique. DNA extracted from a single B. schroederi egg could be successfully amplified, while no cross-reactivity was found with DNA from Ancylostoma caninum eggs. The detection rate of the PCR assay was 68%, which was higher than that of the traditional egg flotation (46%). Our findings demonstrated that the PCR assay is sensitive and specific for the detection and identification of B. schroederi eggs. Therefore, it could become a useful tool for the investigation of B. schroederi infections in giant pandas.

Molecular diagnosis of Baylisascaris schroederi infections in giant panda (Ailuropoda melanoleuca) feces using PCR.

The helminth Baylisascaris schroederi is one of the most harmful parasites infecting giant pandas (Ailuropoda melanoleuca). It is therefore important to develop an exact diagnostic technique to detect this parasite. Using a known number (1, 2, 3, 4, 5, 10, 25, 50, 100) of feces-isolated B. schroederi egg and adult DNA, we developed a PCR to detect a portion of the mitochondrial 12S rRNA and applied it to giant panda fecal samples. The method was sufficiently sensitive to detect B. schroederi DNA from isolated eggs in a fecal sample with a detection threshold of one egg. We detected B. schroederi in 88% of fecal samples, 30% higher than the conventional flotation technique. No cross-reactivity with other common nematode DNA was detected. Our PCR assay may constitute a valuable alternative for the diagnosis of B. schroederi infections.