Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID 19, continues to evolve since its first emergence in December 2019. Using the complete sequences of 1,932 SARS-CoV-2 genomes, various clustering analyses consistently identified six types of the strains. Independent of the dendrogram construction, 13 signature variations in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5' untranslated region (UTR) were identified and provided a direct interpretation for the six types (types I to VI). The six types of the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228 and 38,248 SARS-CoV-2 genomes which became available later. To date, type VI, characterized by the four signature SNVs C241T (5'UTR), C3037T (nsp3 F924F), C14408T (nsp12 P4715L), and A23403G (Spike D614G), with strong allelic associations, has become the dominant type. Since C241T is in the 5' UTR with uncertain significance and the characteristics can be captured by the other three strongly associated SNVs, we focus on the other three. The increasing frequency of the type VI haplotype 3037T-14408T-23403G in the majority of the submitted samples in various countries suggests a possible fitness gain conferred by the type VI signature SNVs. The fact that strains missing one or two of these signature SNVs fail to persist implies possible interactions among these SNVs. Later SNVs such as G28881A, G28882A, and G28883C have emerged with strong allelic associations, forming new subtypes. This study suggests that SNVs may become an important consideration in SARS-CoV-2 classification and surveillance.

PRAP1 is a novel lipid-binding protein that promotes lipid absorption by facilitating MTTP-mediated lipid transport.

Microsomal triglyceride transfer protein (MTTP) is an endoplasmic reticulum resident protein that is essential for the assembly and secretion of triglyceride (TG)-rich, apoB-containing lipoproteins. Although the function and structure of mammalian MTTP have been extensively studied, how exactly MTTP transfers lipids to lipid acceptors and whether there are other biomolecules involved in MTTP-mediated lipid transport remain elusive. Here we identify a role in this process for the poorly characterized protein PRAP1. We report that PRAP1 and MTTP are partially colocalized in the endoplasmic reticulum. We observe that PRAP1 directly binds to TG and facilitates MTTP-mediated lipid transfer. A single amino acid mutation at position 85 (E85V) impairs PRAP1's ability to form a ternary complex with TG and MTTP, as well as impairs its ability to facilitate MTTP-mediated apoB-containing lipoprotein assembly and secretion, suggesting that the ternary complex formation is required for PRAP1 to facilitate MTTP-mediated lipid transport. PRAP1 is detectable in chylomicron/VLDL-rich plasma fractions, suggesting that MTTP recognizes PRAP1-bound TG as a cargo and transfers TG along with PRAP1 to lipid acceptors. Both PRAP1-deficient and E85V knock-in mutant mice fed a chow diet manifested an increase in the length of their small intestines, likely to compensate for challenges in absorbing lipid. Interestingly, both genetically modified mice gained significantly less body weight and fat mass when on high-fat diets compared with littermate controls and were prevented from hepatosteatosis. Together, this study provides evidence that PRAP1 plays an important role in MTTP-mediated lipid transport and lipid absorption.

OPATs: Omnibus P-value association tests.

Combining statistical significances (P-values) from a set of single-locus association tests in genome-wide association studies is a proof-of-principle method for identifying disease-associated genomic segments, functional genes and biological pathways. We review P-value combinations for genome-wide association studies and introduce an integrated analysis tool, Omnibus P-value Association Tests (OPATs), which provides popular analysis methods of P-value combinations. The software OPATs programmed in R and R graphical user interface features a user-friendly interface. In addition to analysis modules for data quality control and single-locus association tests, OPATs provides three types of set-based association test: window-, gene- and biopathway-based association tests. P-value combinations with or without threshold and rank truncation are provided. The significance of a set-based association test is evaluated by using resampling procedures. Performance of the set-based association tests in OPATs has been evaluated by simulation studies and real data analyses. These set-based association tests help boost the statistical power, alleviate the multiple-testing problem, reduce the impact of genetic heterogeneity, increase the replication efficiency of association tests and facilitate the interpretation of association signals by streamlining the testing procedures and integrating the genetic effects of multiple variants in genomic regions of biological relevance. In summary, P-value combinations facilitate the identification of marker sets associated with disease susceptibility and uncover missing heritability in association studies, thereby establishing a foundation for the genetic dissection of complex diseases and traits. OPATs provides an easy-to-use and statistically powerful analysis tool for P-value combinations. OPATs, examples, and user guide can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm.

The causal role of elevated uric acid and waist circumference on the risk of metabolic syndrome components.

BACKGROUND/OBJECTIVES: Hyperuricemia has been found to cluster with multiple components of metabolic syndrome (MetS). It is unclear whether hyperuricemia is a downstream result of MetS or may play an upstream role in MetS development. Using the Mendelian randomization (MR) method, we examined the causal relationship between elevated uric acid and the various components of MetS with waist circumference as a positive control. SUBJECTS/METHODS: Data from 10k participants of Taiwan Biobank was used to carry out MR analysis with uric acid risk score (wGRS) and waist circumference wGRS as instrumental variables and components of MetS as the outcomes. RESULTS: We found that genetically increased serum uric acid corresponds to a significant increment of triglyceride (ß = 0.065, p < 0.0001), systolic blood pressure (ß = 1.047, p = 0.0005), diastolic blood pressure (ß = 0.857, p < 0.0001), and mean arterial pressure (ß = 0.920, p < 0.0001), but a significant reduction of high-density lipoprotein cholesterol (ß = -0.020, p = 0.0014). Uric acid wGRS was not associated with fasting serum glucose, HbA1C, waist circumference, or BMI. On the other hand, waist circumference was causally associated with all the components of MetS including uric acid. CONCLUSIONS: Our MR investigation shows that uric acid increment may augment the risk of MetS through increasing blood pressure and triglyceride levels and lowering HDL-C value but not through accumulating fat or hyperglycemia. High waist circumference may be a causal agent for all the components of MetS including hyperuricemia.

Vegetable Signatures Derived from Human Urinary Metabolomic Data in Controlled Feeding Studies.

Examination of changes in urinary metabolomic profiles after vegetable ingestion may lead to new methods of assessing plant food intake. To this regard, we developed a proof-of-principle methodology to identify urinary metabolomic signatures for spinach, celery, and onion. Three feeding studies were conducted. In the first study, healthy individuals were fed with spinach, celery, onion, and no vegetables in four separate experiments with pooled urinary samples for metabolite discovery. The same protocol was used to validate the finding at the individual level in the second study and when feeding all three vegetables simultaneously in the third study. An LC-MS-based metabolomics approach was adopted to search for indicative metabolites from urine samples collected during multiple time periods before and after the meal. Consequently, a total of 1, 9, and 3 nonoverlapping urinary metabolites were associated with the intake of spinach, celery, and onion, respectively. The PCA signature of these metabolites followed a similar "time cycle" pattern, which maximized at approximately 2-4 h after intake. In addition, the metabolite profiles for the same vegetable were consistent across samples, regardless of whether it was consumed individually or in combination. The developed methodology along with the identified urinary metabolomic signatures were potential tools for assessing plant food intake.

Genome-wide association study of morbid obesity in Han Chinese.

BACKGROUND: As obesity is becoming pandemic, morbid obesity (MO), an extreme type of obesity, is an emerging issue worldwide. It is imperative to understand the factors responsible for huge weight gain in certain populations in the modern society. Very few genome-wide association studies (GWAS) have been conducted on MO patients. This study is the first MO-GWAS study in the Han-Chinese population in Asia. METHODS: We conducted a two-stage GWAS with 1110 MO bariatric patients (body mass index [BMI] ≥ 35 kg/m2) from Min-Sheng General Hospital, Taiwan. The first stage involved 575 patients, and 1729 sex- and age-matched controls from the Taiwan Han Chinese Cell and Genome Bank. In the second stage, another 535 patients from the same hospital were genotyped for 52 single nucleotide polymorphisms (SNPs) discovered in the first stage, and 9145 matched controls from Taiwan Biobank were matched for confirmation analysis. RESULTS: The results of the joint analysis for the second stage revealed six top ranking SNPs, including rs8050136 (p-value = 7.80 × 10- 10), rs9939609 (p-value = 1.32 × 10- 9), rs1421085 (p-value = 1.54 × 10- 8), rs9941349 (p-value = 9.05 × 10- 8), rs1121980 (p-value = 7.27 × 10- 7), and rs9937354 (p-value = 6.65 × 10- 7), which were all located in FTO gene. Significant associations were also observed between MO and RBFOX1, RP11-638 L3.1, TMTC1, CBLN4, CSMD3, and ERBB4, respectively, using the Bonferroni correction criteria for 52 SNPs (p < 9.6 × 10- 4). CONCLUSION: The most significantly associated locus of MO in the Han-Chinese population was the well-known FTO gene. These SNPs located in intron 1, may include the leptin receptor modulator. Other significant loci, showing weak associations with MO, also suggested the potential mechanism underlying the disorders with eating behaviors or brain/neural development.

Genome-Wide Pharmacogenomic Study on Methadone Maintenance Treatment Identifies SNP rs17180299 and Multiple Haplotypes on CYP2B6, SPON1, and GSG1L Associated with Plasma Concentrations of Methadone R- and S-enantiomers in Heroin-Dependent Patients.

Methadone maintenance treatment (MMT) is commonly used for controlling opioid dependence, preventing withdrawal symptoms, and improving the quality of life of heroin-dependent patients. A steady-state plasma concentration of methadone enantiomers, a measure of methadone metabolism, is an index of treatment response and efficacy of MMT. Although the methadone metabolism pathway has been partially revealed, no genome-wide pharmacogenomic study has been performed to identify genetic determinants and characterize genetic mechanisms for the plasma concentrations of methadone R- and S-enantiomers. This study was the first genome-wide pharmacogenomic study to identify genes associated with the plasma concentrations of methadone R- and S-enantiomers and their respective metabolites in a methadone maintenance cohort. After data quality control was ensured, a dataset of 344 heroin-dependent patients in the Han Chinese population of Taiwan who underwent MMT was analyzed. Genome-wide single-locus and haplotype-based association tests were performed to analyze four quantitative traits: the plasma concentrations of methadone R- and S-enantiomers and their respective metabolites. A significant single nucleotide polymorphism (SNP), rs17180299 (raw p = 2.24 × 10(-8)), was identified, accounting for 9.541% of the variation in the plasma concentration of the methadone R-enantiomer. In addition, 17 haplotypes were identified on SPON1, GSG1L, and CYP450 genes associated with the plasma concentration of methadone S-enantiomer. These haplotypes accounted for approximately one-fourth of the variation of the overall S-methadone plasma concentration. The association between the S-methadone plasma concentration and CYP2B6, SPON1, and GSG1L were replicated in another independent study. A gene expression experiment revealed that CYP2B6, SPON1, and GSG1L can be activated concomitantly through a constitutive androstane receptor (CAR) activation pathway. In conclusion, this study revealed new genes associated with the plasma concentration of methadone, providing insight into the genetic foundation of methadone metabolism. The results can be applied to predict treatment responses and methadone-related deaths for individualized MMTs.

An integrated analysis tool for analyzing hybridization intensities and genotypes using new-generation population-optimized human arrays.

BACKGROUND: Affymetrix Axiom single nucleotide polymorphism (SNP) arrays provide a cost-effective, high-density, and high-throughput genotyping solution for population-optimized analyses. However, no public software is available for the integrated genomic analysis of hybridization intensities and genotypes for this new-generation population-optimized genotyping platform. RESULTS: A set of statistical methods was developed for an integrated analysis of allele frequency (AF), allelic imbalance (AI), loss of heterozygosity (LOH), long contiguous stretch of homozygosity (LCSH), and copy number variation or alteration (CNV/CNA) on the basis of SNP probe hybridization intensities and genotypes. This study analyzed 3,236 samples that were genotyped using different SNP platforms. The proposed AF adjustment method considerably increased the accuracy of AF estimation. The proposed quick circular binary segmentation algorithm for segmenting copy number reduced the computation time of the original segmentation method by 30-67 %. The proposed CNV/CNA detection, which integrates AI and LOH/LCSH detection, had a promising true positive rate and well-controlled false positive rate in simulation studies. Moreover, our real-time quantitative polymerase chain reaction experiments successfully validated the CNVs/CNAs that were identified in the Axiom data analyses using the proposed methods; some of the validated CNVs/CNAs were not detected in the Affymetrix Array 6.0 data analysis using the Affymetrix Genotyping Console. All the analysis functions are packaged into the ALICE (AF/LOH/LCSH/AI/CNV/CNA Enterprise) software. CONCLUSIONS: ALICE and the used genomic reference databases, which can be downloaded from http://hcyang.stat.sinica.edu.tw/software/ALICE.html , are useful resources for analyzing genomic data from the Axiom and other SNP arrays.

SMART: Statistical Metabolomics Analysis-An R Tool.

Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm .

Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.

BACKGROUND: Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL). RESULTS: In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations. CONCLUSIONS: In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.

Common and ethnic-specific genetic determinants of hemoglobin concentration between Taiwanese Han Chinese and European Whites: findings from comparative two-stage genome-wide association studies.

Human iron nutrition is a result of interplays between genetic and environmental factors. However, there has been scarcity of data on the genetic variants associated with altered iron homeostasis and ethnic-specific associations are further lacking. In this study, we compared between the Taiwanese Han Chinese (HC) and European Whites the genetic determinants of hemoglobin (Hb) concentration, a biochemical parameter that in part reflects the amount of functional iron in the body. Through sex-specific two-stage genome-wide association studies (2S-GWAS), we observed the consistent Hb-association of SNPs in TMPRSS6 (chr 22), ABO (chr 9), and PRKCE (chr 2) across sexes in both ethnic groups. Specific to the Taiwanese HC, the Hb-association of AXIN1, together with other loci near the chr 16 alpha-globin gene cluster, was found novel. On the other hand, majority of the Hb-associated SNPs among Europeans were identified along the chr 6 major histocompatibility complex (MHC) region, which has established roles in immune system control. We report here strong Hb-associations of HFE and members of gene families (SLC17; H2A, H2B, H3, H4, H1; TRIM; ZSCAN, ZKSCAN, ZNF; HLA; BTN, OR), numerous SNPs in/nearby CARMIL1, PRRC2A, PSORS1C1, NOTCH4, TSBP1, C6orf15, and distinct associations with non-coding RNA genes. Our findings provide evidence for both common and ethnic-specific genetic determinants of Hb between East Asians and Caucasians. These will help to further our understanding of the iron and/or erythropoiesis physiology in humans and to identify high risk subgroups for iron imbalances - a primary requirement to meet the goal of precision nutrition for optimal health.

Exploring Factors Underlying Poorly-Controlled Asthma in Adults by Integrating Phenotypes and Genotypes Associated with Obesity and Asthma: A Case-Control Study.

Background: Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies. Methods: This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction. Results: General obesity with body mass index (BMI) ≥27 kg/m2 (OR:1.49, 95% confidence interval (CI) 1.09-2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22-2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17-2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways. Conclusion: The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.

Modifications of lipid pathways restrict SARS-CoV-2 propagation in human induced pluripotent stem cell-derived 3D airway organoids.

BACKGROUND: Modifications of lipid metabolism were closely associated with the manifestations and prognosis of coronavirus disease of 2019 (COVID-19). Pre-existing metabolic conditions exacerbated the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection while modulations of aberrant lipid metabolisms alleviated the manifestations. To elucidate the underlying mechanisms, an experimental platform that reproduces human respiratory physiology is required. METHODS: Here we generated induced pluripotent stem cell-derived airway organoids (iPSC-AOs) that resemble the human native airway. Single-cell sequencing (ScRNAseq) and microscopic examination verified the cellular heterogeneity and microstructures of iPSC-AOs, respectively. We subjected iPSC-AOs to SARS-CoV-2 infection and investigated the treatment effect of lipid modifiers statin drugs on viral pathogenesis, gene expression, and the intracellular trafficking of the SARS-CoV-2 entry receptor angiotensin-converting enzyme-2 (ACE-2). RESULTS: In SARS-CoV-2-infected iPSC-AOs, immunofluorescence staining detected the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins and bioinformatics analysis further showed the aberrant enrichment of lipid-associated pathways. In addition, SARS-CoV-2 hijacked the host RNA replication machinery and generated the new isoforms of a high-density lipoprotein constituent apolipoprotein A1 (APOA1) and the virus-scavenging protein deleted in malignant brain tumors 1 (DMBT1). Manipulating lipid homeostasis using cholesterol-lowering drugs (e.g. Statins) relocated the viral entry receptor angiotensin-converting enzyme-2 (ACE-2) and decreased N protein expression, leading to the reduction of SARS-CoV-2 entry and replication. The same lipid modifications suppressed the entry of luciferase-expressing SARS-CoV-2 pseudoviruses containing the S proteins derived from different SARS-CoV-2 variants, i.e. wild-type, alpha, delta, and omicron. CONCLUSIONS: Together, our data demonstrated that modifications of lipid pathways restrict SARS-CoV-2 propagation in the iPSC-AOs, which the inhibition is speculated through the translocation of ACE2 from the cell membrane to the cytosol. Considering the highly frequent mutation and generation of SARS-CoV-2 variants, targeting host metabolisms of cholesterol or other lipids may represent an alternative approach against SARS-CoV-2 infection.

LOHAS: loss-of-heterozygosity analysis suite.

Detection of loss of heterozygosity (LOH) plays an important role in genetic, genomic and cancer research. We develop computational methods to estimate the proportion of homozygous SNP calls, identify samples with structural alterations and/or unusual genotypic patterns, cluster samples with close LOH structures and map the genomic segments bearing LOH by analyzing data of genome-wide SNP arrays or customized SNP arrays. In addition to cancer genetics/genomics, we also apply the methods to study long contiguous stretches of homozygosity (LCSH) in general populations. The LCSH analysis aids in the identification of samples with complex LCSH patterns indicative of nonrandom mating and/or meiotic recombination cold spots, separation of samples with different genetic backgrounds and sex, and mapping of regions of LCSH. Affymetrix Human Mapping 500K Set SNP data from an acute lymphoblastic leukemia study containing 304 cancer patients and 50 normal controls and from the HapMap Project containing 30 African trios, 30 Caucasian trios and 90 independent Asian samples were analyzed. We identified common gene regions of LOH, e.g., ETV6 and CDKN1B, and identified frequent regions of LCSH, e.g., the region that encompasses the centromeric gene desert region of chromosome 16. Unsupervised analysis separated cancer subtypes and ethnic subpopulations by patterns of LOH/LCSH. Simulation studies considering LOH width, effect size and heterozygous interference fraction were performed, and the results show that the proposed LOH association test has good test power and controls type 1 error well. The developed algorithms are packaged into LOHAS written in R and R GUI.

Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations.

BACKGROUND: Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations. RESULTS: We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels. CONCLUSIONS: Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.

Evaluating statistical significance in a meta-analysis by using numerical integration.

Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy of statistical significance estimation. In this study, we proposed a numerical integration method and examined its theoretical properties. Simulation studies were conducted to evaluate its Type I error, statistical power, computational efficiency, and estimation accuracy, and the results were compared with those of other methods. The results demonstrate that our proposed method performs well in terms of Type I error, statistical power, computing efficiency (regardless of sample size), and statistical significance estimation accuracy. P-value data from multiple large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs) were analyzed. The results demonstrate that our proposed method can be used to identify critical genomic regions associated with rheumatoid arthritis and asthma, increase statistical significance in individual GWASs and TWASs, and control for false-positives more effectively than can Fisher's method under an independence assumption. We created the software package Pbine, available at GitHub (https://github.com/Yinchun-Lin/Pbine).

Identification of Serum Oxylipins Associated with the Development of Coronary Artery Disease: A Nested Case-Control Study.

Coronary artery disease (CAD) is among the leading causes of death globally. The American Heart Association recommends that people should consume more PUFA-rich plant foods to replace SFA-rich ones to lower serum cholesterol and prevent CAD. However, PUFA may be susceptible to oxidation and generate oxidized products such as oxylipins. In this study, we investigated whether the blood oxylipin profile is associated with the risk of developing CAD and whether including identified oxylipins may improve the predictability of CAD risk. We designed a nested case-control study with 77 cases and 148 matched controls from a 10-year follow-up of the Nutrition and Health Survey in a Taiwanese cohort of 720 people aged 50 to 70. A panel of 46 oxylipins was measured for baseline serum samples. We discovered four oxylipins associated with CAD risk. 13-oxo-ODE, which has been previously found in formed plagues, was positively associated with CAD (OR = 5.02, 95%CI = 0.85 to 15.6). PGE2/PGD2, previously shown to increase cardiac output, was inversely associated (OR = 0.16, 95%CI = 0.06 to 0.42). 15-deoxy-PGJ2, with anti-inflammatory and anti-apoptosis effects on cardiomyocytes (OR = 0.26, 95%CI = 0.09 to 0.76), and 5-HETE, which was associated with inflammation (OR = 0.28, 95%CI = 0.10 to 0.78), were also negatively associated as protective factors. Adding these four oxylipins to the traditional risk prediction model significantly improved CAD prediction.