Association Analysis Approach Research Articles

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. While past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. Here, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semi-parametric generalized additive model (GAM) on fitting tumor volume data from over 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models, the latter is often considered the standard growth model. On the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that eGaIT, a GAM derived efficacy metric, is equivalent to eGR, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on Paclitaxel, anti-PD-1 antibody, Cetuximab, Irinotecan, and Sorafenib, we show that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared eGR-based association analysis and exponential modeling approach in biomarker discovery and found they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.

Quantitative Trait Mapping for Zebra-stem in Tomato Confirms a Genetic Cause Involving the Interaction of Unlinked Loci

Zebra-stem of tomato is a disorder characterized by leaf necrosis, wilting, and a stripped pattern on stems of mature plants. Wilting, necrosis, and death of seedlings are also observed. The physiological and genetic causes of zebra-stem are poorly characterized. Anecdotal evidence has suggested pedigrees with S. pimpinellifolium and bacterial speck resistance in the genetic background are often prone to this disorder. We demonstrate a genetic cause using composite interval mapping and association analysis approaches to define quantitative trait loci (QTLs) that contribute to the disorder. A biparental population of F4 partially inbred families was developed for the initial analysis, and four subsequent backcross or F2 populations were used for subsequent validation. Significant QTLs on chromosomes 5 and 10 were identified, explaining ∼60% and 40% of the variation, respectively. Polymorphisms in the Pto locus are strongly associated with the QTL on chromosome 5. The two loci were derived from different parentage, and a significant interaction effect was demonstrated, resulting in the characteristic zebra-stem symptoms when combined.

A multivariate to multivariate approach for voxel-wise genome-wide association analysis.

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

Exploring the yield and yield contributing traits variability in dolichos bean (Lablab purpureus): A character association and path analysis approach

An experiment was conducted during 2019–20 and 2020–21 at ICAR-Central Institute for Subtropical Horticulture, Lucknow, Uttar Pradesh to evaluate the magnitude of variability in dolichos bean [Lablab purpureus (L.) Sweet] genotypes for important horticultural traits and their associations. The observed range and mean variations of the studied traits revealed a significant genetic diversity among the genotypes. The narrow gap in the genotypic (GCV) and the phenotypic coefficient of variation (PCV) for delineated characters indicated that they are governed by genetic factors, and environmental factors had little or no impact on the phenotypic expression of these traits. The traits including pod yield, days taken to first flowering, weight of pod, plant height, and pod width, showed positive GCV, indicating the presence of additive genes action. Traits such as pod weight, days taken to first flowering, and pod width displayed high heritability and moderate genetic advance, suggesting that the high heritability of these traits is due to the effects of additive genes and that genetic gain can be expected through the phenotypic selection. The higher genotypic correlations revealed an inherent association between the traits. Therefore, the selection criterion for high yield was proposed based on the strong character association between plant height, number of pods/cluster, pod width, and pod weight. The number of pods/plant, followed by the number of clusters/plant had the highest direct positive effect on yield. Consequently, pod yield may be increased through selection of heavier pods, more clusters/ plant, and more pods/cluster.

Efficient Two-Stage Analysis for Complex Trait Association with Arbitrary Depth Sequencing Data

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing association tests on the called genotypes. Standard approaches require accurate genotype calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose a computationally efficient two-stage combination approach for association analysis, in which single-nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML)-based method on sequence data directly (without first calling genotypes), and then the selected SNPs are evaluated in the second stage by performing association tests on genotypes from multi-sample LD-aware calling. Extensive simulation- and real data-based studies show that the proposed two-stage approaches can save 80% of the computational costs and still obtain more than 90% of the power of the classical method to genotype all markers at various depths d≥2.

Genome-wide association and RNA-seq analyses identify loci for pod orientation in rapeseed (Brassica napus).

Spatial distribution and orientation of pods on the main raceme (stem) and branches could affect rapeseed yield. However, genomic regions underlying the pod orientation were not described in Brassica species. Here, we determined the extent of genetic variation in pod orientation, described as the angles of pedicel on raceme (APR) and angles of the pod on pedicel (APP) among 136 rapeseed accessions grown across three environments of the upper, middle and lower Yangtze River in China. The APR ranged from 59° to 109°, while the APP varied from 142° to 178°. Statistical analysis showed that phenotypic variation was due to genotypic (G) and environmental (E) effects. Using the genome-wide association analysis (GWAS) approach, two QTLs for APR (qBnAPR.A02 and qBnAPR.C02) and two for APP (qBnAPP.A05 and qBnAPP.C05), having minor to moderate allelic effects (4.30% to 19.47%) were identified. RNA-seq analysis revealed 606 differentially expressed genes (DEGs) in two rapeseed accessions representing the extreme phenotypes for pod orientation and different alleles at the QTLs of APR. Three DEGs (BnLAZY4.A02, BnSAUR32.A02, and BnSAUR32.C02) were identified as the most likely candidates responsible for variation in pod orientation (APR). This study elucidates the genomic regions and putative candidate genes underlying pod orientation in B. napus.

Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.

Uncovering the Most and the Least Factors Affecting Elderly Health Using Association Mining

Thailand has entered the aging society since 2005 and is now stepping towards a complete aging society in 2021. The elderly’s health problems are difficult to avoid.The research aims to analyze the most influential and most minor powerful factors on the elderly’s health status. The association analysis approach is applied to discover the relationship between various factors that affect good health. 17,804 Thai elderly data from the National Statistical Office (NSO) of Thailand were employed. One hundred ninety-nine features were taken into the association rules mining modeling. FP-growth algorithm is chosen to find the most and the least factors affecting the health of the elderly. The interesting relationships among those factors are also disclosed. As a result, the feature that demonstrates good health is performing daily activities independently. Additionally, several features that are less likely to appear in healthy seniors are hard work, working at risk, living in an urban society, living with unfamiliar caregivers, having a few children, and lacking sufficient government medical care. The knowledge gained from the findings can be considered for preparing health care for the aging society.

Uncovering the Most and the Least Factors Affecting Elderly Health Using Association Mining

Thailand has entered the aging society since 2005 and is now stepping towards a complete aging society in 2021. The elderly’s health problems are difficult to avoid.The research aims to analyze the most influential and most minor powerful factors on the elderly’s health status. The association analysis approach is applied to discover the relationship between various factors that affect good health. 17,804 Thai elderly data from the National Statistical Office (NSO) of Thailand were employed. One hundred ninety-nine features were taken into the association rules mining modeling. FP-growth algorithm is chosen to find the most and the least factors affecting the health of the elderly. The interesting relationships among those factors are also disclosed. As a result, the feature that demonstrates good health is performing daily activities independently. Additionally, several features that are less likely to appear in healthy seniors are hard work, working at risk, living in an urban society, living with unfamiliar caregivers, having a few children, and lacking sufficient government medical care. The knowledge gained from the findings can be considered for preparing health care for the aging society.

A novel age‐informed approach for genetic association analysis in Alzheimer’s disease

The increased risk of Alzheimer's Disease (AD) with age is well established. However, genome-wide association studies of AD have often wrongly accounted for this known effect by covarying by age using age-at-onset for cases and age-at-last-exam for controls. In most scenarios, this leads to controls being on average older than cases and the regression model incorrectly infers that age decreases AD risk. Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases) (Table 1). Modelling variable AD risk across age results in 10-20% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss (Figure 1). Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3 and TAOK2 genes (Figure 2, Tables 2, 3). Incorrect age adjustment may remain unnoticed when the age difference between cases/controls is small and when the sample size is sufficient to identify causal variants with adequate statistical power. If the age difference is large, however, as in the whole-exome sequencing of the ADSP for which controls are 10 years older than cases, then it is highly detrimental to adjust by age. Here we have proposed a novel AD-age score to correctly integrate the age information in the phenotype and showed that it led to a gain in statistical power compared to a traditional logistic regression without age adjustment.

Association and Gene-Gene Interactions Study of Late-Onset Alzheimer's Disease in the Russian Population.

Alzheimer’s disease (AD) is a neurodegenerative disorder, and represents the most common cause of dementia. In this study, we performed several different analyses to detect loci involved in development of the late onset AD in the Russian population. DNA samples from 472 unrelated subjects were genotyped for 63 SNPs using iPLEX Assay and real-time PCR. We identified five genetic loci that were significantly associated with LOAD risk for the Russian population (TOMM40 rs2075650, APOE rs429358 and rs769449, NECTIN rs6857, APOE ε4). The results of the analysis based on comparison of the haplotype frequencies showed two risk haplotypes and one protective haplotype. The GMDR analysis demonstrated three significant models as a result: a one-factor, a two-factor and a three-factor model. A protein–protein interaction network with three subnetworks was formed for the 24 proteins. Eight proteins with a large number of interactions are identified: APOE, SORL1, APOC1, CD33, CLU, TOMM40, CNTNAP2 and CACNA1C. The present study confirms the importance of the APOE-TOMM40 locus as the main risk locus of development and progress of LOAD in the Russian population. Association analysis and bioinformatics approaches detected interactions both at the association level of single SNPs and at the level of genes and proteins.

Epigenome-wide analysis of DNA methylation and coronary heart disease: a nested case-control study.

Identifying environmentally responsive genetic loci where DNA methylation is associated with coronary heart disease (CHD) may reveal novel pathways or therapeutic targets for CHD. We conducted the first prospective epigenome-wide analysis of DNA methylation in relation to incident CHD in the Asian population. We did a nested case-control study comprising incident CHD cases and 1:1 matched controls who were identified from the 10 year follow-up of the China Kadoorie Biobank. Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip. We performed the single cytosine-phosphate-guanine (CpG) site association analysis and network approach to identify CHD-associated CpG sites and co-methylation gene module. After quality control, 982 participants (mean age 50.1 years) were retained. Methylation level at 25 CpG sites across the genome was associated with incident CHD (genome-wide false discovery rate [FDR] < 0.05 or module-specific FDR < 0.01). One SD increase in methylation level of identified CpGs was associated with differences in CHD risk, ranging from a 47 % decrease to a 118 % increase. Mediation analyses revealed 28.5 % of the excessed CHD risk associated with smoking was mediated by methylation level at the promoter region of ANKS1A gene (P for mediation effect = 0.036). Methylation level at the promoter region of SNX30 was associated with blood pressure and subsequent risk of CHD, with the mediating proportion to be 7.7 % (P = 0.003) via systolic blood pressure and 6.4 % (P = 0.006) via diastolic blood pressure. Network analysis revealed a co-methylation module associated with CHD. We identified novel blood methylation alterations associated with incident CHD in the Asian population and provided evidence of the possible role of epigenetic regulations in the smoking- and blood pressure-related pathways to CHD risk. This work was supported by National Natural Science Foundation of China (81390544 and 91846303). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (202922/Z/16/Z, 088158/Z/09/Z, 104085/Z/14/Z), grant (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key R&D Program of China, and Chinese Ministry of Science and Technology (2011BAI09B01).

High resolution mapping and candidate gene identification of downy mildew race 16 resistance in spinach

BackgroundDowny mildew, the most devastating disease of spinach (Spinacia oleracea L.), is caused by the oomycete Peronospora effusa [=P. farinosa f. sp. spinaciae]. The P. effusa shows race specificities to the resistant host and comprises 19 reported races and many novel isolates. Sixteen new P. effusa races were identified during the past three decades, and the new pathogen races are continually overcoming the genetic resistances used in commercial cultivars. A spinach breeding population derived from the cross between cultivars Whale and Lazio was inoculated with P. effusa race 16 in an environment-controlled facility; disease response was recorded and genotyped using genotyping by sequencing (GBS). The main objective of this study was to identify resistance-associated single nucleotide polymorphism (SNP) markers from the cultivar Whale against the P. effusa race 16.ResultsAssociation analysis conducted using GBS markers identified six significant SNPs (S3_658,306, S3_692697, S3_1050601, S3_1227787, S3_1227802, S3_1231197). The downy mildew resistance locus from cultivar Whale was mapped to a 0.57 Mb region on chromosome 3, including four disease resistance candidate genes (Spo12736, Spo12784, Spo12908, and Spo12821) within 2.69–11.28 Kb of the peak SNP.ConclusionsGenomewide association analysis approach was used to map the P. effusa race 16 resistance loci and identify associated SNP markers and the candidate genes. The results from this study could be valuable in understanding the genetic basis of downy mildew resistance, and the SNP marker will be useful in spinach breeding to select resistant lines.

A novel age-informed approach for genetic association analysis in Alzheimer\u2019s disease

BackgroundMany Alzheimer’s disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.MethodsUsing simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).ResultsModeling variable AD risk across age results in 5–10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes.ConclusionOur AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments.

Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.

Beta‐defensin 1 gene polymorphisms in the pathologies of the oral cavity—Data from meta‐analysis: Association only with rs1047031 not with rs1800972, rs1799946, and rs11362

The purpose of this meta-analysis was to reveal a potential association of the four functional polymorphisms in human Beta-defensin 1 (DEFB1) gene: rs1047031(c*5G>A) at 3'UTR and rs11362 (-20 G>A), rs1800972(-44 C>G), and rs1799946 (-52 G>A) at 5'UTR with the risk of common oral cavity pathologies that included periodontitis, caries, lichen planus, and recurrent aphthous stomatitis. The relevant studies were obtained by the two researchers from PubMed, Scopus, and Web of Science up to April 29, 2020. The manual search of the reference lists was also performed. Studies on DEFB1 gene polymorphisms and oral cavity disorders, using the case-control genetic association analysis approach, and published as full texts in English were included. To assess the association strength, odds ratios (ORs) with their 95% confidence intervals (CIs) were extracted. Thirteen publications met the inclusion criteria and were incorporated in this meta-analysis. Statistically significant values of the association tests were found only for the rs1047031 polymorphism. Allele distribution in the rs1047031 polymorphism was significantly associated with susceptibility to oral cavity pathologies (adjusted P value=0.003). The rare variant allele carriers had a significantly higher risk for oral disasters under recessive (CC vs CT+TT), and CC vs CT models. No significant correlations between rs11362, rs1800972, and rs1799946 and the risk of oral pathologies were revealed. Significant association between rs1047031 polymorphism and risk of oral pathologies has been found, and therefore, we suggest to include this polymorphism in future research concerning the genetic background of the oral cavity diseases.

Variational Bayesian inference for association over phylogenetic trees for microorganisms

ABSTRACT With the advance of next generation sequencing technologies, researchers now routinely obtain a collection of microbial sequences with complex phylogenetic relationships. It is often of interest to analyze the association between certain environmental factors and characteristics of the microbial collection. Though methods have been developed to test for association between the microbial composition with environmental factors as well as between coevolving traits, a flexible model that can provide a comprehensive picture of the relationship between microbial community characteristics and environmental variables will be tremendously beneficial. We developed a Bayesian approach for association analysis while incorporating the phylogenetic structure to account for the dependence between observations. To overcome the computational difficulty related to the phylogenetic tree, a variational algorithm was developed to evaluate the posterior distribution. As the posterior distribution can be readily obtained for parameters of interest and any derived variables, the association relationship can be examined comprehensively. With two application examples, we demonstrated that the Bayesian approach can uncover nuanced details of the microbial assemblage with regard to the environmental factor. The proposed Bayesian approach and variational algorithm can be extended for other problems involving dependence over tree-like structures.

An Empirical Study on Forensic Analysis of Urdu Text Using LDA-Based Authorship Attribution

In the recent years, text-based digital forensic has evolved into a major research domain that supports digital investigation. A piece of text can be a critical source of information that is written by somebody with respect to writing style, usage of typical vocabulary, and so on. In this paper, we present a unified approach for intelligent association analysis of text of how much a piece of text is related to a person with respect to his stylometric writing features. The latent Dirichlet allocation (LDA)-based approach emphasizes on instance-based and profile-based classification of an author’s text. Here, LDA suitably handles the high dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing styles of authors, and the inherent ambiguity of Urdu language text. A large corpus was collected for performance testing of the presented approach. The results of the experiments show the superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authorship attribution. Manifold contributions of the presented paper are use of improved sqrt-cosine similarity with LDA topics to measure similarity in vectors of text documents for the forensic analysis purpose, construction of a large data set of 6000 documents of articles, and achievement of (92% f1-measure) results on articles without using any labels for authorship attribution task.

A powerful approach reveals numerous expression quantitative trait haplotypes in multiple tissues.

Recently many studies showed single nucleotide polymorphisms (SNPs) affect gene expression and contribute to development of complex traits/diseases in a tissue context-dependent manner. However, little is known about haplotype's influence on gene expression and complex traits, which reflects the interaction effect between SNPs. In the present study, we firstly proposed a regulatory region guided eQTL haplotype association analysis approach, and then systematically investigate the expression quantitative trait loci (eQTL) haplotypes in 20 different tissues by the approach. The approach has a powerful design of reducing computational burden by the utilization of regulatory predictions for candidate SNP selection and multiple testing corrections on non-independent haplotypes. The application results in multiple tissues showed that haplotype-based eQTLs not only increased the number of eQTL genes in a tissue specific manner, but were also enriched in loci that associated with complex traits in a tissue-matched manner. In addition, we found that tag SNPs of eQTL haplotypes from whole blood were selectively enriched in certain combination of regulatory elements (e.g. promoters and enhancers) according to predicted chromatin states. In summary, this eQTL haplotype detection approach, together with the application results, shed insights into synergistic effect of sequence variants on gene expression and their susceptibility to complex diseases. The executable application 'eHaplo' is implemented in Java and is publicly available at http://grass.cgs.hku.hk/limx/ehaplo/. Supplementary data are available at Bioinformatics online.

A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.