Improved Statistical Methods for Genetic Association Studies

Genetic association studies have successfully identified numerous genetic variants associated with complex diseases and gene expression levels, providing unprecedented opportunities to discover new biology through downstream analyses. Examples of such analyses include multivariate methods, which can enhance the power to detect signals by borrowing information across similar or correlated conditions; fine-mapping analysis, which aims to identify potentially causal loci among many highly correlated genetic variants; and Mendelian randomization, which estimates the causal effect of one trait on another using genetic variants as instruments. In this dissertation, we focus on improving methods for downstream analysis, with the goal of enhancing the power of statistical inference. In Chapter 2, we improve the fitting algorithm of a widely used multivariate method, the multivariate adaptive shrinkage (MASH) by Urbut et al. [2019]. In Chapter 3, we develop a new method for fine-mapping time-to-event outcomes, building on the existing "Sum of Single Effects" (SuSiE) fine-mapping approach by Wang et al. [2020]. In Chapter 4, we address the challenges associated with the small sample sizes of within-family genotype data. Specifically, we develop methods to improve the efficiency of estimates derived from within-family data, which also lead to variance reduction in Mendelian randomization

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models

Boosting microscopic object detection via feature activation map guided poisson blending

Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications

Effects of shading on photosynthetic characteristics of wax apple leaves

The wax apple (Syzygium samarangense) is a highly valuable fruit species in Southeast Asia. To regulate the fruiting season, shading is commonly used to induce flowering in wax apple. However, the effects of shading on the growth of wax apple is not well understood. To address this, we conducted a study analyzing the photosynthetic characteristics of wax apple leaves under 40% and 90% shading rates. Our findings revealed that shading had a significant impact on the photosynthesis and branching tip development of wax apple. During shading treatments, the chlorophyll contents of the leaves increased to enhance light absorption efficiency. In the 40% shading treatment, the primary factor causing the decrease in net photosynthetic rate was stomatal limitation, while in the 90% shading treatment, both stomatal and non-stomatal limitations contributed to the decrease in net photosynthetic rate. These results are indications that sheading plays a key role in chlorophyll and photosynthesis in wax apple. These results will have led to a new research direction for genetic crop improvement

Th17-related cytokines contribute to recall-like expansion/effector function of HMBPP-specific Vγ2Vδ2 T cells after Mycobacterium tuberculosis infection or vaccination: Immunity to infection

Whether cytokines can influence the adaptive immune response by antigen-specific γδ T cells during infections or vaccinations remains unknown. We previously demonstrated that, during BCG/Mycobacterium tuberculosis (Mtb) infections, Th17-related cytokines markedly upregulated when phosphoantigen-specific VγVδ2 T cells expanded. In this study, we examined the involvement of Th17-related cytokines in the recall-like responses of Vγ2Vδ2 T cells following Mtb infection or vaccination against TB. Treatment with IL-17A/IL-17F or IL-22 expanded phosphoantigen 4-hydroxy-3-methyl-but-enyl pyrophosphate (HMBPP)-stimulated Vγ2Vδ2 T cells from BCG-vaccinated macaques but not from naïve animals, and IL-23 induced greater expansion than the other Th17-related cytokines. Consistently, Mtb infection of macaques also enhanced the ability of IL-17/IL-22 or IL-23 to expand HMBPP-stimulated Vγ2Vδ2 T cells. When evaluating IL-23 signaling as a prototype, we found that HMBPP/IL-23-expanded Vγ2Vδ2 T cells from macaques infected with Mtb or vaccinated with BCG or Listeria ΔactA prfA*-ESAT6/Ag85B produced IL-17, IL-22, IL-2, and IFN-γ. Interestingly, HMBPP/IL-23-induced production of IFN-γ in turn facilitated IL-23-induced expansion of HMBPP-activated Vγ2Vδ2 T cells. Furthermore, HMBPP/IL-23-induced proliferation of Vγ2Vδ2 T cells appeared to require APC contact and involve the conventional and novel protein kinase C signaling pathways. These findings suggest that Th17-related cytokines can contribute to recall-like expansion and effector function of Ag-specific γδ T cells after infection or vaccination

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge's goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68\% to 86.70\%, and 70.42\% to 73.44\% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://segrap2023.grand-challenge.orgComment: A challenge report of SegRap2023 (organized in conjunction with MICCAI2023

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD