A Prism Vote method for individualized risk prediction of traits in genotype data of Multi-population.

Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is described by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as genetic heterogeneity and sample size increase. In two real genome-wide association data consists of multiple populations, we showed that the framework considerably enhanced prediction accuracy of the linear mixed model in five-group cross validations. The proposed method offers a new aspect to analyze individual's disease risk and improve accuracy for predicting complex traits in genotype data.

Immune modulation by rural exposures and allergy protection.

BACKGROUND: Growing up on traditional farms protects children from the development of asthma and allergies. However, we have identified distinct asthma-protective factors, such as poultry exposure. This study aims to examine the biological effect of rural exposure in China. METHODS: We recruited 67 rural children (7.4 ± 0.9 years) and 79 urban children (6.8 ± 0.6 years). Depending on the personal history of exposure to domestic poultry (DP), rural children were further divided into those with DP exposure (DP+ , n = 30) and those without (DP- , n = 37). Blood samples were collected to assess differential cell counts and expression of immune-related genes. Dust samples were collected from poultry stables inside rural households. In vivo activities of nasal administration of DP dust extracts were tested in an ovalbumin-induced asthma model. RESULTS: There was a stepwise increase in the percentage of eosinophils (%) from rural DP+ children (median = 1.65, IQR = [1.28, 3.75]) to rural DP- children (3.40, [1.70, 6.50]; DP+ vs. DP- , p = .087) and to the highest of their urban counterparts (4.00, [2.00, 7.25]; urban vs. DP+ , p = .017). Similarly, rural children exhibited reduced mRNA expression of immune markers, both at baseline and following lipopolysaccharide (LPS) stimulation. Whereas LPS stimulation induced increased secretion of Th1 and proinflammatory cytokines in rural DP+ children compared to rural DP- children and urban children. Bronchoalveolar lavage of mice with intranasal instillation of dust extracts from DP household showed a significant decrease in eosinophils as compared to those of control mice (p < .05). Furthermore, DP dust strongly inhibited gene expression of Th2 signature cytokines and induced IL-17 expression in the murine asthma model. CONCLUSIONS: Immune responses of rural children were dampened compared to urban children and those exposed to DP had further downregulated immune responsiveness. DP dust extracts ameliorated Th2-driven allergic airway inflammation in mice. Determining active protective components in the rural environment may provide directions for the development of primary prevention of asthma.

Comparing the incubation period, serial interval, and infectiousness profile between SARS-CoV-2 Omicron and Delta variants.

In January 2022, the SARS-CoV-2 Omicron variants initiated major outbreaks and dominated the transmissions in Hong Kong, displacing an earlier outbreak seeded by the Delta variants. To provide insight into the transmission potential of the emerging variants, we aimed to compare the epidemiological characteristics of the Omicron and Delta variants. We analyzed the line-list clinical and contact tracing data of the SARS-CoV-2 confirmed cases in Hong Kong. Transmission pairs were constructed based on the individual contact history. We fitted bias-controlled models to the data to estimate the serial interval, incubation period and infectiousness profile of the two variants. Viral load data were extracted and fitted to the random effect models to investigate the potential risk modifiers for the clinical viral shedding course. Totally 14 401 confirmed cases were reported between January 1 and February 15, 2022. The estimated mean serial interval (4.4 days vs. 5.8 days) and incubation period (3.4 days vs. 3.8 days) were shorter for the Omicron than the Delta variants. A larger proportion of presymptomatic transmission was observed for the Omicron (62%) compared to the Delta variants (48%). The Omicron cases had higher mean viral load over an infection course than the Delta cases, with the elder cases appearing more infectious than the younger cases for both variants. The epidemiological features of Omicron variants were likely an obstacle to contact tracing measures, imposed as a major intervention in settings like Hong Kong. Continuously monitoring the epidemiological feature for any emerging SARS-CoV-2 variants in the future is needed to assist officials in planning measures for COVID-19 control.

Association of spirometric restriction with mortality in the silicotics: a cohort study.

BACKGROUND: Restrictive spirometry pattern (RSP), defined as reduced forced vital capacity (FVC) in absence of airflow obstruction (AFO), is associated with increased risk of mortality in general population. However, evidence in the patients with silicosis is limited. This study was aimed to investigate the relationship between RSP and the risk of death in a silicotic cohort. METHOD: This retrospective cohort study used data from the Pneumoconiosis Clinic, Hong Kong Department of Health that containing 4315 patients aged 18-80 years and diagnosed with silicosis during 1981-2019, with a follow-up till 31 December 2019. Spirometry was carried out at the diagnostic examination of silicosis. Lung function categories were classified as normal spirometry (FEV1/FVC ≥ 0.7, FVC ≥ 80% predicted), RSP only (FEV1/FVC ≥ 0.7, FVC < 80% predicted), AFO only (FEV1/FVC < 0.7, FVC ≥ 80% predicted), and RSP&AFO mixed (FEV1/FVC < 0.7, FVC < 80% predicted). The hazard ratio (HR) and 95% confidence intervals (95% CI) were computed using a Cox proportional hazards model adjusting for age, body mass index, history of tuberculosis, smoking status, pack-years, and radiographic characteristics of silicotic nodules. RESULTS: Among the 4315 patients enrolled in the study, the prevalence of RSP was 24.1% (n = 1038), including 11.0% (n = 473) with RSP only and 13.1% (n = 565) with mixed RSP and AFO. During the follow-up period, a total of 2399 (55.6%) deaths were observed. Compared with the silicotics with normal spirometry, those with RSP only had significantly increased risk of all-cause mortality (HR = 1.63, 95% CI 1.44-1.85) and respiratory-related mortality (HR = 1.56, 95% CI 1.31-1.85). Notably, a higher risk of mortality was observed in silicotics with mixed ventilatory defects of both RSP and AFO (all-cause mortality: HR = 2.22, 95% CI 1.95-2.52; respiratory-related mortality: HR = 2.59, 95% CI 2.18-3.07) than in those with RSP only. CONCLUSION: RSP is significantly associated with increased risk of all-cause and respiratory-related mortality in the silicotics, and patients with mixed restrictive and obstructive ventilatory defect have higher risk of mortality than those with single RSP or AFO. These findings emphasize the importance of recognizing RSP in the occupational settings, especially for the silicotic patients with mixed ventilatory defect.

Long-term effectiveness of elderly health care voucher scheme strategies: a system dynamics simulation analysis.

BACKGROUND: The elderly healthcare voucher (EHCV) scheme is expected to lead to an increase in the number of elderly people selecting private primary healthcare services and reduce reliance on the public sector in Hong Kong. However, studies thus far have reported that this scheme has not received satisfactory responses. In this study, we examined changes in the ratio of visits between public and private doctors in primary care (to measure reliance on the public sector) for different strategic scenarios in the EHCV scheme. METHODS: Based on comments from an expert panel, a system dynamics model was formulated to simulate the impact of various enhanced strategies in the scheme: increasing voucher amounts, lowering the age eligibility, and designating vouchers for chronic conditions follow-up. Data and statistics for the model calibration were collected from various sources. RESULTS: The simulation results show that the current EHCV scheme is unable to reduce the utilization of public healthcare services, as well as the ratio of visits between public and private primary care among the local aging population. When comparing three different tested scenarios, even if the increase in the annual voucher amount could be maintained at the current pace or the age eligibility can be lowered to include those aged 60 years, the impact on shifts from public-to-private utilization were insignificant. The public-to-private ratio could only be marginally reduced from 0.74 to 0.64 in the first several years. Nevertheless, introducing a chronic disease-oriented voucher could result in a significant drop of 0.50 in the public-to-private ratio during the early implementation phase. However, the effect could not be maintained for an extended period. CONCLUSIONS: Our findings will assist officials in improving the design of the EHCV scheme, within the wider context of promoting primary care among the elderly. We suggest that an additional chronic disease-oriented voucher can serve as an alternative strategy. The scheme must be redesigned to address more specific objectives or provide a separate voucher that promotes under-utilized healthcare services (e.g., preventive care), instead of services designed for unspecified reasons, which may lead to concerns regarding exploitation.

Statistical methods for genome-wide association studies.

Genome-wide association studies (GWAS) detect common genetic variants associated with complex disorders. With their comprehensive coverage of common single nucleotide polymorphisms and comparatively low cost, GWAS are an attractive tool in the clinical and commercial genetic testing. This review introduces the pipeline of statistical methods used in GWAS analysis, from data quality control, association tests, population structure control, interaction effects and results visualization, through to post-GWAS validation methods and related issues.

Epidemiological changes in measles infections in southern China between 2009 and 2016: a retrospective database analysis.

BACKGROUND: The incidence rate of measles in China reached a nadir in 2012 after 2 supplementary immunization activities (SIAs) were undertaken in 2009 and 2010. However, the disease began re-emerging in 2013, with a high prevalence rate observed in 2013-2014 in the southern province of Guangdong. In this study, we assessed the changes that occurred in measles epidemiology during 2009-2016, particularly between 2009 and 2011 (when the influence of the SIAs were in full effect) and between 2012 and 2016 (when this influence subsided). METHODS: Data from 22,362 patients with measles diagnosed between 2009 and 2016, and whose diagnoses were confirmed clinically and/or with laboratory testing, were extracted from the National Infectious Disease Monitoring Information System. Descriptive analyses were performed, and changes in epidemiological characteristics between 2009 and 2011 and 2012-2016 were compared. RESULTS: There was a substantial surge in 0-8-month-old patients after 2012; the incidence rate increased from 4.0 per 100,000 population in 2011 (10.3% of the total) to 280 per 100,000 population in 2013 (32.8% of the total). Patients aged 0-6 years represented 73.4% of the total increase between 2011 and 2013. Compared with 2009-2011, adults aged ≥25 years accounted for a higher proportion of patients in 2013 and after (p < 0.01), and were highest in 2016 (31% of the patient total). CONCLUSION: Despite the remarkable results achieved by SIAs in terms of providing herd immunity, the 2013 resurgence of measles revealed insufficient immunization coverage among children. Therefore routine immunization programs should be strengthened, and supplementary vaccinations targeting adults should also be contemplated.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests.

MOTIVATION: Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power. RESULTS: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. AVAILABILITY AND IMPLEMENTATION: The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html. CONTACT: maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting responses to treatment with fenofibrate in pedigrees.

BACKGROUND: Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees. RESULTS: The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks. CONCLUSIONS: Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb;

Incorporating methylation genome information improves prediction accuracy for drug treatment responses.

BACKGROUND: An accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified. RESULTS: A stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%. CONCLUSIONS: Compared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.

A fast and powerful W-test for pairwise epistasis testing.

Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a combined log odds ratio. The test is model-free, fast, and inherits a Chi-squared distribution with data adaptive degrees of freedom. No permutation is needed to obtain the P-values. Simulation studies demonstrated that the W-test is more powerful in low frequency variants environment than alternative methods, which are the Chi-squared test, logistic regression and multifactor-dimensionality reduction (MDR). In two independent real bipolar disorder genome-wide associations (GWAS) datasets, the W-test identified significant interactions pairs that can be replicated, including SLIT3-CENPN, SLIT3-TMEM132D, CNTNAP2-NDST4 and CNTCAP2-RTN4R The genes in the pairs play central roles in neurotransmission and synapse formation. A majority of the identified loci are undiscoverable by main effect and are low frequency variants. The proposed method offers a powerful alternative tool for mapping the genetic puzzle underlying complex disorders.

Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data.

Genetic data consists of a wide range of marker types, including common, low-frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a dataset-adaptive W-test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample size was not large enough to draw conclusions for the lack of predictability of low-frequency variants and their epistasis.

A W-test collapsing method for rare-variant association testing in exome sequencing data.

Advancement in sequencing technology enables the study of association between complex disorder phenotypes and single-nucleotide polymorphisms with rare mutations. However, the rare genetic variant has extremely small variance and impairs testing power of traditional statistical methods. We introduce a W-test collapsing method to evaluate rare-variant association by measuring the distributional differences between cases and controls through combined log of odds ratio within a genomic region. The method is model-free and inherits chi-squared distribution with degrees of freedom estimated from bootstrapped samples of the data, and allows for fast and accurate P-value calculation without the need of permutations. The proposed method is compared with the Weighted-Sum Statistic and Sequence Kernel Association Test on simulation datasets, and showed good performances and significantly faster computing speed. In the application of real next-generation sequencing dataset of hypertensive disorder, it identified genes of interesting biological functions associated to metabolism disorder and inflammation, including the MACROD1, NLRP7, AGK, PAK6, and APBB1. The proposed method offers an efficient and effective way for testing rare genetic variants in whole exome sequencing datasets.

A statistical method utilizing information of imported cases to estimate the transmissibility for an influenza pandemic.

BACKGROUND: In a new influenza pandemic, travel data such as arrival times of cases seeded by the originating country can be regarded as a combination of the epidemic size and the mobility networks of infections connecting the originating country with other regions. It can be a complete and timely source for estimating the basic reproduction number (R 0 ), a key indicator of disease transmissibility. METHOD: In this study, we developed a likelihood-based method using arrival times of infected cases in different countries to estimate R 0 for influenza pandemics. A simulation was conducted to assess the performance of the proposed method. We further applied the method to the outbreak of the influenza pandemic A/H1N1 in Mexico. RESULTS: In the numerical application, the estimated R 0 was equal to 1.69 with a 95% confidence interval (1.65, 1.73). For the simulation results, the estimations were robust to the decline of travel rate and other parameter assumptions. Nevertheless, the estimates were moderately sensitive to the assumption of infectious duration. Generally, the findings were in line with other relevant studies. CONCLUSIONS: Our approach as well as the estimate is potential to assist officials in planning control and prevention measures. Improved coordination to streamline or even centralize surveillance of imported cases among countries will thus be beneficial to public health.

Genetic Test, Risk Prediction, and Counseling.

Advancement in technology has nurtured the new era of genetic tests for personalized medicine. In this chapter, we will introduce the current development, challenges, and the outlook of genetic test, disease risk prediction, and genetic counseling. In the first section, we will present the success cases in the areas of molecular classification of tumors, pharmacogenomics, and Mendelian disorders, and the challenges of genetic tests implementations. In the second section, common methods for genetic risk prediction models and evaluation measures will be introduced, as well as challenges in feature reliability, risk model stability, and clinical utility. In the final section, key components of genetic counseling will be introduced, covering individual communications, psychosocial concerns, risk assessments, and follow-ups. Current evidences have shown a promising future for genetic testing and risk prediction; we expect that the advancement of analytical methods, technology, integration of omics data, and the increasing clinical implementation and regulation will continue to pave the way for precision medicine in future.

Dietary intake of manganese and the risk of the metabolic syndrome in a Chinese population.

Animal studies have suggested that Mn might be associated with some components of the metabolic syndrome (MetS). A few epidemiological studies have assessed dietary Mn intake and its association with the risk of the MetS and its components among Chinese adults. In this study, we assessed daily dietary Mn intake and its relationship with MetS risk among Chinese adults in Zhejiang Province using data from the 5th Chinese National Nutrition and Health Survey (2010-2012). A total of 2111 adults were included. Dietary Mn intake was assessed using 3-d 24-h dietary recalls; health-related data were obtained by questionnaire surveys, physical examinations and laboratory assessments. The mean intake of Mn was 6·07 (sd 2·94) mg/d for men (n 998) and 5·13 (sd 2·65) mg/d for women (n 1113). Rice (>42 %) was the main food source of Mn. The prevalence of the MetS was 28·0 % (590/2111). Higher Mn intake was associated with a decreased risk of the MetS in men (Q4 v. Q1 OR 0·62; 95 % CI 0·42, 0·92; P trend=0·043) but an increased risk in women (Q4 v. Q1 OR 1·56; 95 % CI 1·02, 2·45; P trend=0·078). In addition, Mn intake was inversely associated with abdominal obesity (P trend=0·016) and hypertriacylglycerolaemia (P trend=0·029) in men, but positively associated with low HDL-cholesterol in both men (P trend=0·003) and women (P trend<0·001). Our results suggest that higher Mn intakes may be protective against the MetS in men. The inverse association between Mn intake and the MetS in women might be due to the increased risk for low HDL-cholesterol.

Association of smoking cessation with airflow obstruction in workers with silicosis: A cohort study.

BACKGROUND: Studies in general population reported a positive association between tobacco smoking and airflow obstruction (AFO), a hallmark of chronic obstructive pulmonary disease (COPD). However, this attempt was less addressed in silica dust-exposed workers. METHODS: This retrospective cohort study consisted of 4481 silicotic workers attending the Pneumoconiosis Clinic during 1981-2019. The lifelong work history and smoking habits of these workers were extracted from medical records. Spirometry was carried out at the diagnosis of silicosis (n = 4177) and reperformed after an average of 9.4 years of follow-up (n = 2648). AFO was defined as forced expiratory volume in one second (FEV1)/force vital capacity (FVC) less than lower limit of normal (LLN). The association of AFO with smoking status was determined using multivariate logistics regression, and the effect of smoking cessation on the development of AFO was evaluated Cox regression. RESULTS: Smoking was significantly associated with AFO (current smokers: OR = 1.92, 95% CI 1.51-2.44; former smokers: OR = 2.09, 95% CI 1.65-2.66). The risk of AFO significantly increased in the first 3 years of quitting smoking (OR = 1.23, 95% CI 1.02-1.47) but decreased afterwards with increasing years of cessation. Smoking cessation reduced the risk of developing AFO no matter before or after the confirmation of silicosis (pre-silicosis cessation: HR = 0.58, 95% CI 0.46-0.74; post-silicosis cessation: HR = 0.62, 95% CI 0.48-0.79). CONCLUSIONS: Smoking cessation significantly reduced the risk of AFO in the workers with silicosis, although the health benefit was not observed until 3 years of abstinence. These findings highlight the importance of early and long-term smoking cessation among silicotic or silica dust-exposed workers.

Quantitative and qualitative subgenomic RNA profiles of SARS-CoV-2 in respiratory samples: A comparison between Omicron BA.2 and non-VOC-D614G.

The SARS-CoV-2 Omicron variants are notorious for their transmissibility, but little is known about their subgenomic RNA (sgRNA) expression. This study applied RNA-seq to delineate the quantitative and qualitative profiles of canonical sgRNA of 118 respiratory samples collected from patients infected with Omicron BA.2 and compared with 338 patients infected with non-variant of concern (non-VOC)-D614G. A unique characteristic profile depicted by the relative abundance of 9 canonical sgRNAs was reproduced by both BA.2 and non-VOC-D614G regardless of host gender, age and presence of pneumonia. Remarkably, such profile was lost in samples with low viral load, suggesting a potential application of sgRNA pattern to indicate viral activity of individual patient at a specific time point. A characteristic qualitative profile of canonical sgRNAs was also reproduced by both BA.2 and non-VOC-D614G. The presence of a full set of canonical sgRNAs carried a coherent correlation with crude viral load (AUC â€‹= â€‹0.91, 95% CI 0.88-0.94), and sgRNA ORF7b was identified to be the best surrogate marker allowing feasible routine application in characterizing the infection status of individual patient. Further potentials in using sgRNA as a target for vaccine and antiviral development are worth pursuing.

Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.

Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.