Supplementary data are available at Bioinformatics online.

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.

The Arabidopsis thaliana WRKY proteins are characterized by a sequence of 60 amino acids including WRKY domain. It is well established that these proteins are involved in the regulation of various physiological programs unique to plants including pathogen defense, senescence and response to environmental stresses, which attracts attention of the scientific community as to how this family might have evolved. We tried to satisfy this curiosity and analyze reasons for duplications of these gene sequences leading to their diversified gene actions. The WRKY sequences available in Arabidopsis thaliana were used to evaluate selection pressure following duplication events. A phylogenetic tree was constructed and the WRKY family was divided into five sub-families. After that, tests were conducted to decide whether positive or purified selection played key role in these events. Our results suggest that purifying selection played major role during the evolution of this family. Some amino acid changes were also detected in specific branches of phylogeny suggesting that relaxed constraints might also have contributed to functional divergence among sub-families. Sites relaxed from purifying selection were identified and mapped onto the structural and functional regions of the WRKY1 protein. These analyses will enhance our understanding of the precise role played by natural selection to create functional diversity in WRKY family.

Current treatments for diabetic ulcers (DUs) remain unsatisfactory due to the risk of bacterial infection and impaired angiogenesis during the healing process. The increased degradation of polyubiquitinated hypoxia‐inducible factor‐1α (HIF‐1α) compromises wound healing efficacy. Therefore, the maintenance of HIF‐1α protein stability might help treat DU. Nitric oxide (NO) is an intrinsic biological messenger that functions as a ubiquitination flow repressor and antibacterial agent; however, its clinical application in DU treatment is hindered by the difficulty in controlling NO release. Here, an intelligent near‐infrared (NIR)‐triggered NO nanogenerator (SNP@MOF‐UCNP@ssPDA‐Cy7/IR786s, abbreviated as SNP@UCM) is presented. SNP@UCM represses ubiquitination‐mediated proteasomal degradation of HIF‐1α by inhibiting its interaction with E3 ubiquitin ligases under NIR irradiation. Increased HIF‐1α expression in endothelial cells by SNP@UCM enhances angiogenesis in wound sites, promoting vascular endothelial growth factor (VEGF) secretion and cell proliferation and migration. SNP@UCM also enables early detection of wound infections and ROS‐mediated killing of bacteria. The potential clinical utility of SNP@UCM is further demonstrated in infected full‐thickness DU model under NIR irradiation. SNP@UCM is the first reported HIF‐1α‐stabilizing advanced nanomaterial, and further materials engineering might offer a facile, mechanism‐based method for clinical DU management.

Allopatry is conventionally considered the geographical mode of speciation for continental island organisms. However, strictly allopatric speciation models that assume the lack of postdivergence gene flow seem oversimplified given the recurrence of land bridges during glacial periods since the late Pliocene. Here, to evaluate whether a continental island endemic, the Taiwan hwamei (Leucodioptron taewanus, Passeriformes Timaliidae) speciated in strict allopatry, we used weighted-regression-based approximate Bayesian computation (ABC) to analyse the genetic polymorphism of 18 neutral nuclear loci (total length: 8500 bp) in Taiwan hwamei and its continental sister species, the Chinese hwamei (L. canorum canorum). The nonallopatry model was found to fit better with observed genetic polymorphism of the two hwamei species (posterior possibility = 0.82). We also recovered unambiguous signals of nontrivial bidirectional postdivergence gene flow (N(e)m >> 1) between Chinese hwamei and Taiwan hwamei until 0.5 Ma. Divergence time was estimated to be 3.5 to 2 million years earlier than that estimated from mitochondrial cytochrome b sequences. Finally, using the inferred nonallopatry model to simulate genetic variation at 24 nuclear genes examined showed that the adiponectin receptor 1 gene may be under divergent adaptation. Our findings imply that the role of geographical barrier may be less prominent for the speciation of continental island endemics, and suggest a shift in speciation studies from simply correlating geographical barrier and genetic divergence to examining factors that facilitate and maintain divergence, e.g. differential selection and sexual selection, especially in the face of interpopulation gene flow.

No abstract