Blood lipid levels are heritable, treatable risk factors for cardiovascular disease. We systematically assessed genome-wide coding variation to identify novel lipid genes, fine-map known lipid loci, and evaluate whether low frequency variants with large effect exist. Using an exome array, we genotyped 80,137 coding variants in 5,643 Norwegians. We followed up 18 variants in 4,666 Norwegians to identify 10 loci with coding variants associated with a lipid trait (P < 5×10−8). One coding variant in TM6SF2 (p.Glu167Lys), residing in a GWAS locus for lipid levels, modifies total cholesterol levels and is associated with myocardial infarction. Transient overexpression and knockdown of TM6SF2 in mouse produces alteration in serum lipid profiles consistent with the association observed in humans, identifying TM6SF2 as the functional gene at a large GWAS locus previously known as NCAN/CILP2/PBX4 or 19p13. This study demonstrates that systematic assessment of coding variation can quickly point to a candidate causal gene.

To address the complex interactions between humans and wildlife habitat, we developed a conceptual framework that links human factors with forested landscapes and wildlife habitat. All the components in the framework are integrated into systems models that analyze the effects of human factors and project how wildlife habitat would change under different policy scenarios. As a case study, we applied this framework to the Wolong Nature Reserve in Sichuan Province (southwestern China), the largest home of the giant panda (Ailuropoda melanoleuca). We collected ecological and socioeconomic data with a combination of various methods ( field observations, aerial photographs, government documents and statistics, interviews, and household surveys) and employed geographic information systems and systems modeling to analyze and integrate the data sources. Human population size has increased by 66% and the number of households in the reserve has increased by 115% since 1975, when the reserve was established. During the same period, the quality and quantity of the giant panda habitat dramatically decreased because of increasing human activities such as fuelwood collection. Systems modeling predicted that under the status quo, human population in the reserve would continue to grow and cause more destruction of the remaining panda habitat, whereas reducing human birth rates and increasing human emigration rates would lower human population size and alleviate human impacts on the panda habitat. Furthermore, our simulations and surveys suggested that policies encouraging the emigration of young people would be more effective and feasible than relocating older people in reducing human population size and conserving giant panda habitat in the reserve.

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The independent dietary shift from carnivore to herbivore with over 90% being bamboo in the giant and the red pandas is of great interests to biologists. Although previous studies have shown convergent evolution of the giant and the red pandas at both morphological and molecular level, the evolution of the gut microbiota in these pandas remains largely unknown. The goal of this study was to determine whether the gut microbiota of the pandas converged due to the same diet, or diverged. We characterized the fecal microbiota from these two species by pyrosequencing the 16S V1–V3 hypervariable regions using the 454 GS FLX Titanium platform. We also included fecal samples from Asian black bears, a species phylogenetically closer to the giant panda, in our analyses. By analyzing the microbiota from these 3 species and those from other carnivores reported previously, we found the gut microbiotas of the giant pandas are distinct from those of the red pandas and clustered closer to those of the black bears. Our data suggests the divergent evolution of the gut microbiota in the pandas.

Thanks to the availability of multiomics data of individual cancer patients, precision medicine or personalized medicine is becoming a promising treatment for individual cancer patients. However, the association patterns, that is, the mechanism of response (MoR) between large-scale multiomics features and drug response are complex and heterogeneous and remain unclear. Although there are existing computational models for predicting drug response using the high-dimensional multiomics features, it remains challenging to uncover the complex molecular mechanism of drug responses. To reduce the number of predictors/features and make the model more interpretable, in this study, 46 signaling pathways were used to build a deep learning model constrained by signaling pathways, consDeepSignaling, for anti–drug response prediction. Multiomics data, like gene expression and copy number variation, of individual genes can be integrated naturally in this model. The signaling pathway–constrained deep learning model was evaluated using the multiomics data of ∼1000 cancer cell lines in the Broad Institute Cancer Cell Line Encyclopedia (CCLE) database and the corresponding drug–cancer cell line response data set in the Genomics of Drug Sensitivity in Cancer (GDSC) database. The evaluation results showed that the proposed model outperformed the existing deep neural network models. Also, the model interpretation analysis indicated the distinctive patterns of importance of signaling pathways in anticancer drug response prediction.