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Polygenic scores (PGS) quantify genetic contributions to complex traits, yet existing single- and multi-ancestry methods lack multi-dimensional evaluation within a unified framework. Here, we benchmarked 13 state-of-the-art PGS methods across 36 traits in UK Biobank European and African samples. The prediction performance, computational efficiency, the number of variants, and the impact of different linkage disequilibrium (LD) reference sizes were thoroughly assessed for each method. Results of single-ancestry methods demonstrate that LDpred2 has superior performance across a broad spectrum of complex traits in terms of accuracy and computational efficiency; however, other methods remain valuable for specific traits. For multi-ancestry methods, PRS-CSx and X-Wing have comparable performance, whereas LDpred2-multi outperforms both. Notably, we find that increasing the panel size of the LD reference significantly elevates PGS performance for sample sizes below 1,000, and it reaches a plateau when it exceeds 5,000 samples. Furthermore, implementing PGS calculation methods requires considerable technical effort and resource allocation. To support easy use of these PGS methods, we developed a user-friendly online computing platform, PGS-hub, that integrates all evaluated methods and is pre-configured with ancestry-stratified LD panels. This resource enables a scalable and harmonized PGS computation platform for the PGS community. Here the authors systematically benchmark 13 polygenic score (PGS) methods in UK Biobank European and African populations, revealing factors that affect prediction accuracy. They also introduce an automated platform for standardized and reproducible PGS computation.

The ancestors of marine mammals once roamed the land and independently committed to an aquatic lifestyle. These macroevolutionary transitions have intrigued scientists for centuries. Here, we generated high-quality genome assemblies of 17 marine mammals (11 cetaceans and six pinnipeds), including eight assemblies at the chromosome level. Incorporating previously published data, we reconstructed the marine mammal phylogeny and population histories and identified numerous idiosyncratic and convergent genomic variations that possibly contributed to the transition from land to water in marine mammal lineages. Genes associated with the formation of blubber (NFIA), vascular development (SEMA3E), and heat production by brown adipose tissue (UCP1) had unique changes that may contribute to marine mammal thermoregulation. We also observed many lineage-specific changes in the marine mammals, including genes associated with deep diving and navigation. Our study advances understanding of the timing, pattern, and molecular changes associated with the evolution of mammalian lineages adapting to aquatic life.

Understanding the factors that cause endangered populations to either grow or decline is crucial for preserving biodiversity. Conservation efforts often address extrinsic threats, such as environmental degradation and overexploitation, that can limit the recovery of endangered populations. Genetic factors such as inbreeding depression can also affect population dynamics but these effects are rarely measured in the wild and thus often neglected in conservation efforts. Here we show that inbreeding depression strongly influences the population dynamics of an endangered killer whale population, despite genomic signatures of purging of deleterious alleles via natural selection. We find that the ‘Southern Residents’, which are currently endangered despite nearly 50 years of conservation efforts, exhibit strong inbreeding depression for survival. Our population models suggest that this inbreeding depression limits population growth and predict further decline if the population remains genetically isolated and typical environmental conditions continue. The Southern Residents also had more inferred homozygous deleterious alleles than three other, growing, populations, further suggesting that inbreeding depression affects population fitness. These results demonstrate that inbreeding depression can substantially limit the recovery of endangered populations. Conservation actions focused only on extrinsic threats may therefore fail to account for key intrinsic genetic factors that also limit population growth. Genomic and demographic analyses of the ‘Southern Resident’ killer whales in the North Pacific find that strong inbreeding depression is inhibiting growth of this small and isolated population. The findings help to explain why this group of whales is still declining despite 50 years of conservation efforts.

The Mongolian Plateau plays a crucial role in global carbon cycling, but the spatiotemporal characteristics of XCO2 concentration and its driving mechanism remain insufficiently explored. To solve this scientific issue, the synergistic methodology of mathematical statistics—the Pearson correlation and random forest model—was established using the main source of Orbiting Carbon Observatory 2 (OCO-2) satellite data. Results indicate the following: (1) Average XCO2 concentration of the Mongolian Plateau was 412 ppm, with an annual growth rate of 2.29 ppm/a from 2018 to 2022, along with higher values in the south and lower values in the north. The seasonal change displayed a clear temporal feature, in the order of spring (414.83 ppm) > winter (413.4 ppm) > autumn (411.3 ppm) > summer (409.12 ppm). The spatial distributions in spring, autumn, and winter were relatively consistent, all showing higher XCO2 concentrations in the east and lower concentrations in the west, whereas summer exhibited the opposite pattern. (2) From the perspective of the natural environment, XCO2 change was negatively correlated with the normalized difference vegetation index (NDVI), precipitation (PRE), and temperature (TEMP). Temporal analysis further revealed that this negative correlation was most pronounced in the eastern region, in which these three elements were all relatively high. (3) According to the random forest model, the influence of both single and interactive factors on the plateau’s XCO2 varied significantly. A comparison of driving factors revealed that the NDVI had the highest contribution rate (0.35), followed by fossil fuel combustion emissions (ODIAC), wind direction (WD), and wind speed (WS). As for interaction effects, the combination of NDVI and ODIAC showed the highest contribution rate (over 0.25), indicating a strong joint influence on XCO2. Other important interactions included WS and WD, ODIAC and WS, and NDVI and WS (all above 0.05). These findings provide valuable insights into the driving mechanisms of XCO2 on the Mongolian Plateau, offering a reference for regional carbon emission reduction policies.

Development of sharing economy has brought new opportunities to the development of green logistics. Pallet pooling is a typical type of sharing economy. It has been recently promoted in almost every country in the world. Researchers have reached a common agreement that establishing an effective pallet pool is of great importance to the pallets use efficiency. It is also believed that the implementation of pallet pool will substantially contribute to the economic and social development. Detailed analysis of pallet pooling such as benefit analysis, mode choose, pallet allocation model & algorithm, quality control, etc. are discussed in this paper. Based on literature review, several important problems are listed for future study. These problems are information management, supply chain management, rental pricing, pallets tracking and allocation, quality control, sustainable development (carbon emissions), and “internet plus pallet pooling”. Some suggestions on China’s pallet pooling development are proposed as well.

The Mongolian Plateau plays a crucial role in global carbon cycling, but the spatiotemporal characteristics of XCO[sub.2] concentration and its driving mechanism remain insufficiently explored. To solve this scientific issue, the synergistic methodology of mathematical statistics—the Pearson correlation and random forest model—was established using the main source of Orbiting Carbon Observatory 2 (OCO-2) satellite data. Results indicate the following: (1) Average XCO[sub.2] concentration of the Mongolian Plateau was 412 ppm, with an annual growth rate of 2.29 ppm/a from 2018 to 2022, along with higher values in the south and lower values in the north. The seasonal change displayed a clear temporal feature, in the order of spring (414.83 ppm) > winter (413.4 ppm) > autumn (411.3 ppm) > summer (409.12 ppm). The spatial distributions in spring, autumn, and winter were relatively consistent, all showing higher XCO[sub.2] concentrations in the east and lower concentrations in the west, whereas summer exhibited the opposite pattern. (2) From the perspective of the natural environment, XCO[sub.2] change was negatively correlated with the normalized difference vegetation index (NDVI), precipitation (PRE), and temperature (TEMP). Temporal analysis further revealed that this negative correlation was most pronounced in the eastern region, in which these three elements were all relatively high. (3) According to the random forest model, the influence of both single and interactive factors on the plateau’s XCO[sub.2] varied significantly. A comparison of driving factors revealed that the NDVI had the highest contribution rate (0.35), followed by fossil fuel combustion emissions (ODIAC), wind direction (WD), and wind speed (WS). As for interaction effects, the combination of NDVI and ODIAC showed the highest contribution rate (over 0.25), indicating a strong joint influence on XCO[sub.2] . Other important interactions included WS and WD, ODIAC and WS, and NDVI and WS (all above 0.05). These findings provide valuable insights into the driving mechanisms of XCO[sub.2] on the Mongolian Plateau, offering a reference for regional carbon emission reduction policies.

Multiple germline and somatic genomic factors are associated with risk of coronary artery disease, but there is no single measure of risk that integrates all information from a DNA sample. To address this gap, we develop an integrated genomic model that includes six germline and somatic genetic drivers for coronary artery disease, including polygenic risk score, genetically-proxied proteomic/metabolomic risk scores, and clonal hematopoiesis of indeterminate potential. We evaluated its predictive power in the UK Biobank (N = 391,536), and validate it using data from the TOPMed program (N = 34,177). The 10-year coronary artery disease risk based on the integrated genomic model profile ranges from 1.1% to 15.5% in the UK Biobank and from 3.8% to 33.0% in TOPMed, with a more pronounced gradient in males than females. The integrated genomic model captures the cumulative effect of multiple genetic drivers, identifying individuals at high risk for coronary artery disease despite lacking any single high-risk genetic factor, as well as individuals at low risk despite carrying known high-risk factors. In middle age, the integrated genomic model augments the performance of the Pooled Cohort Equations, a clinical risk calculator for coronary artery disease. While the integrated genomic model yields only modest incremental predictive value over polygenic risk score at the population level, it identifies approximately 13% of high-risk individuals not detected by polygenic risk score alone.

The rabbit owns commercial importance in meat and fur production, and also has long served as a valuable animal model in biomedical research. Yet, the complete assembly of a high-quality rabbit reference genome has not been established. Here, we present a telomere-to-telomere (T2T) genome assembly of the New Zealand White (NZW) rabbit, the most complete and accurate rabbit genome to date. Using a haploid embryonic stem cell line (haESC) to overcame the challenges of homologous sequence interference and structural variation, we generate a genome with a contig N50 of 137.71 Mb, anchoring all chromosomes with only three gaps, through integrating long-read sequencing (PacBio HiFi, Oxford Nanopore), Hi-C, and Illumina data. Quality assessments reveal near-complete coverage of the BUSCO mammalian gene set, with 99.61% completeness. Notably, this complete assembly allowed for the first comprehensive annotation of the rabbit immunoglobulin loci and major histocompatibility complex region, which were previously unresolved in earlier genome versions. The NZW-T2T assembly based on haESC not only provides a critical resource for advancing rabbit-based research, but also set a new benchmark for de novo genome assembly for other animals with complex genome.

Invertebrates, animals (metazoans) without backbones, encompass ∼97% of all animal yet remains understudied. They have provided insights into molecular mechanisms underlying fundamentally identical mechanisms in phylogenetically diverse animals, including vertebrates. Marine invertebrates have long fascinated researchers due to their abundance, diversity, adaptations, and impact on ecosystems and human economies. Here, we report a compendium and appraisal of 190 marine invertebrate genomes spanning 21 phyla, 43 classes, 92 orders, and 134 families. We identify a high proportion and long unit size of tandem repeats, likely contributing to reported difficulties in invertebrate genome assembly. A well-supported phylogenetic tree of marine invertebrates from 974 single-copy orthologous genes resolved topological controversies. We show that Ctenophora is at the basal phylum and Porifera is the sister group of Parahoxozoa; that Xenacoelomorpha is within Bilateria and is the sister group to Protostomia, rejecting three out of four hypotheses in the field; and that Bryozoa is at the basal position of Lophotrochozoa, not grouped into Lophophorata. We also present insights into the genetic underpinnings of metazoans from Hox genes, innate immune gene families, and nervous system gene families. Our marine invertebrate genome compendium provides a unified foundation for studies on their evolution and effects on ecological systems and human life.

Chelmon rostratus (Teleostei, Perciformes, Chaetodontidae) is a copperband butterflyfish. As an ornamental fish, the genome information for this species might help understanding the genome evolution of Chaetodontidae and adaptation/evolution of coral reef fish. In this study, using the stLFR linked-read data, we assembled a genome of 638.70 Mb in size with contig and scaffold N50 sizes of 294.41 kb and 2.61 Mb, respectively. 94.40% of scaffold sequences were assigned to 24 chromosomes using Hi-C data and BUSCO analysis showed that 97.3% (2,579) of core genes were found in our assembly. Up to 21.47 % of the genome was found to be repetitive sequences and 21,375 protein-coding genes were annotated. Among these annotated protein-coding genes, 20,163 (94.33%) proteins were assigned with possible functions. As the first genome for Chaetodontidae family, the information of these data helpfully to improve the essential to the further understanding and exploration of marine ecological environment symbiosis with coral and the genomic innovations and molecular mechanisms contributing to its unique morphology and physiological features.