Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

ABSTRACTLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

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The low response rate to immunosuppressant is mainly due to the lack of adequate knowledge about the tumor microenvironment (TME) and screening biomarkers for gliomas. We aimed to identify the promising immune biomarkers and new immune classification of glioma. In this study, multiple‐immune algorithms were used to calculate immune‐infiltration scores. Unsupervised and supervised machine learning methods were used to perform the classification. We observed that OLFML3 overexpression was indicated in gliomas and linked to poor prognosis. OLFML3 knockdown inhibited proliferation, invasion and increased the sensitivity of glioma cells to temozolomide. OLFML3 expression could also reflect the aberrant immune status. Based on the immune‐related signature, patients were divided into three immune subtypes via consensus clustering. Patients with C2 subtype presented poorer prognosis and shorter progression free survival than patients with other two subtypes. The TME patterns among subtypes were different. C2 and C3 subtypes are the immune‐inflamed and immune‐desert tumors, respectively. Additionally, compared with C3 subtype, patients with C1/C2 subtypes were more likely to respond to immunotherapy. The pRRophetic algorithm indicated patients with C1/C2 subtypes were more resistant to temozolomide, but sensitive to paclitaxel and cisplatin. To conclude, OLFML3 overexpression affects glioma cell proliferation, invasion, and TMZ sensitivity and has been proved to be an independent prognostic‐ and immune‐related biomarker. Additionally, the novel immune subtype's classification could provide the prognostic and predictive predictors for glioma patients and may guide physicians in selecting potential responders.

Nitrofuran antibiotics have been widely used in the prevention and treatment of animal diseases due to the bactericidal effect. However, the residual and accumulation of their metabolites in vivo can pose serious health hazards to both humans and animals. Although their usage in feeding and process of food-derived animals have been banned in many countries, their metabolic residues are still frequently detected in materials and products of animal-derived food. Many sensitive and effective detection methods have been developed to deal with the problem. In this work, we summarized various immunological methods for the detection of four nitrofuran metabolites based on different types of detection principles and signal molecules. Furthermore, the development trend of detection technology in animal-derived food is prospected.