A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. RESULTS: Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). CONCLUSIONS: This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.

Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single-parameter model that outperforms the state-of-the-art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user-friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Boosting predictabilities of agronomic traits in rice using bivariate genomic selection.

The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.

The E3 Ubiquitin Ligase HAF1 Modulates Circadian Accumulation of EARLY FLOWERING3 to Control Heading Date in Rice under Long-Day Conditions.

The ubiquitin 26S proteasome system (UPS) is critical for enabling plants to alter their proteomes to integrate internal and external signals for the photoperiodic induction of flowering. We previously demonstrated that HAF1, a C3HC4 RING domain-containing E3 ubiquitin ligase, is essential to precisely modulate the timing of Heading Date1 accumulation and to ensure appropriate photoperiodic responses under short-day conditions in rice (Oryza sativa). However, how HAF1 mediates flowering under long-day conditions remains unknown. In this study, we show that OsELF3 (EARLY FLOWERING3) is the direct substrate of HAF1 for ubiquitination in vitro and in vivo. HAF1 is required for maintaining the circadian rhythm of OsELF3 accumulation during photoperiodic responses in rice. In addition, the haf1 oself3 double mutant headed as late as oself3 plants under long-day conditions. An amino acid variation (L558S) within the interaction domain of OsELF3 with HAF1 greatly contributes to the variation in heading date among japonica rice accessions. The japonica accessions carrying the OsELF3(L)-type allele are found at higher latitudes, while varieties carrying the OsELF3(S)-type allele are found at lower latitudes. Taken together, our findings suggest that HAF1 precisely modulates the diurnal rhythm of OsELF3 accumulation to ensure the appropriate heading date in rice.

Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice.

Rice (Oryza sativa) is an important dietary source of both essential micronutrients and toxic trace elements for humans. The genetic basis underlying the variations in the mineral composition, the ionome, in rice remains largely unknown. Here, we describe a comprehensive study of the genetic architecture of the variation in the rice ionome performed using genome-wide association studies (GWAS) of the concentrations of 17 mineral elements in rice grain from a diverse panel of 529 accessions, each genotyped at ∼6.4 million single nucleotide polymorphism loci. We identified 72 loci associated with natural ionomic variations, 32 that are common across locations and 40 that are common within a single location. We identified candidate genes for 42 loci and provide evidence for the causal nature of three genes, the sodium transporter gene Os-HKT1;5 for sodium, Os-MOLYBDATE TRANSPORTER1;1 for molybdenum, and Grain number, plant height, and heading date7 for nitrogen. Comparison of GWAS data from rice versus Arabidopsis (Arabidopsis thaliana) also identified well-known as well as new candidates with potential for further characterization. Our study provides crucial insights into the genetic basis of ionomic variations in rice and serves as an important foundation for further studies on the genetic and molecular mechanisms controlling the rice ionome.

Genome-wide association studies reveal that members of bHLH subfamily 16 share a conserved function in regulating flag leaf angle in rice (Oryza sativa).

As a major component of ideal plant architecture, leaf angle especially flag leaf angle (FLA) makes a large contribution to grain yield in rice. We utilized a worldwide germplasm collection to elucidate the genetic basis of FLA that would be helpful for molecular design breeding in rice. Genome-wide association studies (GWAS) identified a total of 40 and 32 QTLs for FLA in Wuhan and Hainan, respectively. Eight QTLs were commonly detected in both conditions. Of these, 2 and 3 QTLs were identified in the indica and japonica subpopulations, respectively. In addition, the candidates of 5 FLA QTLs were verified by haplotype-level association analysis. These results indicate diverse genetic bases for FLA between the indica and japonica subpopulations. Three candidates, OsbHLH153, OsbHLH173 and OsbHLH174, quickly responded to BR and IAA involved in plant architecture except for OsbHLH173, whose expression level was too low to be detected; their overexpression in plants increased rice leaf angle. Together with previous studies, it was concluded that all 6 members in bHLH subfamily 16 had the conserved function in regulating FLA in rice. A comparison with our previous GWAS for tiller angle (TA) showed only one QTL had pleiotropic effects on FLA and TA, which explained low similarity of the genetic basis between FLA and TA. An ideal plant architecture is expected to be efficiently developed by combining favorable alleles for FLA from indica with favorable alleles for TA from japonica by inter-subspecies hybridization.

Oxygen Plasma Technology-Assisted Preparation of Three-Dimensional Reduced Graphene Oxide/Polypyrrole/Strontium Composite Scaffold for Repair of Bone Defects Caused by Osteoporosis.

Repairs of bone defects caused by osteoporosis have always relied on bone tissue engineering. However, the preparation of composite tissue engineering scaffolds with a three-dimensional (3D) macroporous structure poses huge challenges in achieving osteoconduction and osteoinduction for repairing bone defects caused by osteoporosis. In the current study, a three-dimensional macroporous (150-300 µm) reduced graphene oxide/polypyrrole composite scaffold modified by strontium (Sr) (3D rGO/PPY/Sr) was successfully prepared using the oxygen plasma technology-assisted method, which is simple, safe, and inexpensive. The findings of the MTT assay and AO/EB fluorescence double staining showed that 3D rGO/PPY/Sr has a good biocompatibility and effectively promoted MC3T3-E1 cell proliferation. Furthermore, the ALP assay and alizarin red staining showed that 3D rGO/PPY/Sr increased the expression levels of ALP activity and the formation of calcified nodules. The desirable biocompatibility, osteoconduction, and osteoinduction abilities, assure that the 3D macroporous rGO/PPY/Sr composite scaffold offers promising potential for use in the repair of bone defects caused by osteoporosis in bone tissue engineering.

A lamin-like protein OsNMCP1 regulates drought resistance and root growth through chromatin accessibility modulation by interacting with a chromatin remodeller OsSWI3C in rice.

Lamin proteins in animals are implicated in important nuclear functions, including chromatin organization, signalling transduction, gene regulation and cell differentiation. Nuclear Matrix Constituent Proteins (NMCPs) are lamin analogues in plants, but their regulatory functions remain largely unknown. We report that OsNMCP1 is localized at the nuclear periphery in rice (Oryza sativa) and induced by drought stress. OsNMCP1 overexpression resulted in a deeper and thicker root system, and enhanced drought resistance compared to the wild-type control. An assay for transposase accessible chromatin with sequencing (ATAC-seq) analysis revealed that OsNMCP1-overexpression altered chromatin accessibility in hundreds of genes related to drought resistance and root growth, including OsNAC10, OsERF48, OsSGL, SNAC1 and OsbZIP23. OsNMCP1 can interact with SWITCH/SUCROSE NONFERMENTING (SWI/SNF) chromatin remodelling complex subunit OsSWI3C. The reported drought resistance or root growth-related genes that were positively regulated by OsNMCP1 were negatively regulated by OsSWI3C under drought stress conditions, and OsSWI3C overexpression led to decreased drought resistance. We propose that the interaction between OsNMCP1 and OsSWI3C under drought stress conditions may lead to the release of OsSWI3C from the SWI/SNF gene silencing complex, thus changing chromatin accessibility in the genes related to root growth and drought resistance.

Analysis of trait heritability in functionally partitioned rice genomes.

Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations. The reported trait-relevant SNPs appear to be arbitrarily distributed along the genome, including genic and nongenic regions. Whether the SNPs in different genomic regions play different roles in trait heritability and which region is more responsible for phenotypic variation remains opaque. We analyzed a natural rice population of 524 accessions with 3,616,597 SNPs to compare the genetic contributions of functionally distinct genomic regions for five agronomic traits, i.e., yield, heading date, plant height, grain length, and grain width. An analysis of heritability in the functionally partitioned rice genome showed that regulatory or intergenic regions account for the most trait heritability. A close look at the trait-associated SNPs (TASs) indicated that the majority of the TASs are located in nongenic regions, and the genetic effects of the TASs in nongenic regions are generally greater than those in genic regions. We further compared the predictabilities using the genetic variants from genic regions with those using nongenic regions. The results revealed that nongenic regions play a more important role than genic regions in trait heritability in rice, which is consistent with findings in humans and maize. This conclusion not only offers clues for basic research to disclose genetics behind these agronomic traits, but also provides a new perspective to facilitate genomic selection in rice.

Genome sequence of the model rice variety KitaakeX.

BACKGROUND: The availability of thousands of complete rice genome sequences from diverse varieties and accessions has laid the foundation for in-depth exploration of the rice genome. One drawback to these collections is that most of these rice varieties have long life cycles, and/or low transformation efficiencies, which limits their usefulness as model organisms for functional genomics studies. In contrast, the rice variety Kitaake has a rapid life cycle (9 weeks seed to seed) and is easy to transform and propagate. For these reasons, Kitaake has emerged as a model for studies of diverse monocotyledonous species. RESULTS: Here, we report the de novo genome sequencing and analysis of Oryza sativa ssp. japonica variety KitaakeX, a Kitaake plant carrying the rice XA21 immune receptor. Our KitaakeX sequence assembly contains 377.6 Mb, consisting of 33 scaffolds (476 contigs) with a contig N50 of 1.4 Mb. Complementing the assembly are detailed gene annotations of 35,594 protein coding genes. We identified 331,335 genomic variations between KitaakeX and Nipponbare (ssp. japonica), and 2,785,991 variations between KitaakeX and Zhenshan97 (ssp. indica). We also compared Kitaake resequencing reads to the KitaakeX assembly and identified 219 small variations. The high-quality genome of the model rice plant KitaakeX will accelerate rice functional genomics. CONCLUSIONS: The high quality, de novo assembly of the KitaakeX genome will serve as a useful reference genome for rice and will accelerate functional genomics studies of rice and other species.

A directed learning strategy integrating multiple omic data improves genomic prediction.

Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic data alone has reached a bottleneck, and previous studies on transcriptomic and metabolomic predictions ignored genomic information. Here, we designed a novel strategy of GP called multilayered least absolute shrinkage and selection operator (MLLASSO) by integrating multiple omic data into a single model that iteratively learns three layers of genetic features (GFs) supervised by observed transcriptome and metabolome. Significantly, MLLASSO learns higher order information of gene interactions, which enables us to achieve a significant improvement of predictability of yield in rice from 0.1588 (GP alone) to 0.2451 (MLLASSO). In the prediction of the first two layers, some genes were found to be genetically predictable genes (GPGs) as their expressions were accurately predicted with genetic markers. Interestingly, we made three dramatic discoveries for the GPGs: (i) GPGs are good predictors for highly complex traits like yield; (ii) GPGs are mostly eQTL genes (cis or trans); and (iii) trait-related transcriptional factor families are enriched in GPGs. These findings support the notion that learned GFs not only are good predictors for traits but also have specific biological implications regarding regulation of gene expressions. To differentiate the new method from conventional GP models, we called MLLASSO a directed learning strategy supervised by intermediate omic data. This new prediction model appears to be more reliable and more robust than conventional GP models.

Genome-wide association analyses reveal the genetic basis of combining ability in rice.

Combining ability is a measure for selecting elite parents and predicting hybrid performance in plant breeding. However, the genetic basis of combining ability remains unclear and a global view of combining ability from diverse mating designs is lacking. We developed a North Carolina II (NCII) population of 96 Oryza sativa and four male sterile lines to identify parents of greatest value for hybrid rice production. Statistical analyses indicated that general combining ability (GCA) and specific combining ability (SCA) contributed variously to different agronomic traits. In a genome-wide association study (GWAS) of agronomic traits, GCA and SCA, we identified 34 significant associations (P < 2.39 × 10-7 ). The superior alleles of GCA loci (Ghd8, GS3 and qSSR4) accumulated in parental lines with high GCA and explained 30.03% of GCA variance in grain yield, indicating that molecular breeding of high GCA parental lines is feasible. The distinct distributions of these QTLs contributed to the differentiation of parental GCA in subpopulations. GWAS of SCA identified 12 more loci that showed dominance on corresponding agronomic traits. We conclude that the accumulation of superior GCA and SCA alleles is an important contributor to heterosis and QTLs that greatly contributed to combining ability in our study would accelerate the identification of elite inbred lines and breeding of super hybrids.

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.

A Novel Tiller Angle Gene, TAC3, together with TAC1 and D2 Largely Determine the Natural Variation of Tiller Angle in Rice Cultivars.

Tiller angle is one of the most important components of the ideal plant architecture that can greatly enhance rice grain yield. Understanding the genetic basis of tiller angle and mining favorable alleles will be helpful for breeding new plant-type varieties. Here, we performed genome-wide association studies (GWAS) to identify genes controlling tiller angle using 529 diverse accessions of Oryza sativa including 295 indica and 156 japonica accessions in two environments. We identified 7 common quantitative trait loci (QTLs), including the previously reported major gene Tiller Angle Control 1 (TAC1), in the two environments, 10 and 13 unique QTLs in Hainan and Wuhan, respectively. More QTLs were identified in indica than in japonica, and three major QTLs (qTA3, qTA1b/DWARF2 (D2) and qTA9c/TAC1) were fixed in japonica but segregating in indica, which explained the wider variation observed in indica compared with that in japonica. No common QTLs were identified between the indica and japonica subpopulations. Mutant analysis for the candidate gene of qTA3 on chromosome 3 indicated a novel gene, Tiller Angle Control 3 (TAC3), encoding a conserved hypothetical protein controlling tiller angle. TAC3 is preferentially expressed in the tiller base. The ebisu dwarf (d2) mutant exhibited a decreased tiller angle, in addition to its previously described abnormal phenotype. A nucleotide diversity analysis revealed that TAC3, D2 and TAC1 have been subjected to selection during japonica domestication. A haplotype analysis identified favorable alleles of TAC3, D2 and TAC1, which may be used for breeding plants with an ideal architecture. In conclusion, there is a diverse genetic basis for tiller angle between the two subpopulations, and it is the novel gene TAC3 together with TAC1, D2, and other newly identified genes in this study that controls tiller angle in rice cultivars.

Extensive sequence divergence between the reference genomes of two elite indica rice varieties Zhenshan 97 and Minghui 63.

Asian cultivated rice consists of two subspecies: Oryza sativa subsp. indica and O. sativa subsp. japonica Despite the fact that indica rice accounts for over 70% of total rice production worldwide and is genetically much more diverse, a high-quality reference genome for indica rice has yet to be published. We conducted map-based sequencing of two indica rice lines, Zhenshan 97 (ZS97) and Minghui 63 (MH63), which represent the two major varietal groups of the indica subspecies and are the parents of an elite Chinese hybrid. The genome sequences were assembled into 237 (ZS97) and 181 (MH63) contigs, with an accuracy >99.99%, and covered 90.6% and 93.2% of their estimated genome sizes. Comparative analyses of these two indica genomes uncovered surprising structural differences, especially with respect to inversions, translocations, presence/absence variations, and segmental duplications. Approximately 42% of nontransposable element related genes were identical between the two genomes. Transcriptome analysis of three tissues showed that 1,059-2,217 more genes were expressed in the hybrid than in the parents and that the expressed genes in the hybrid were much more diverse due to their divergence between the parental genomes. The public availability of two high-quality reference genomes for the indica subspecies of rice will have large-ranging implications for plant biology and crop genetic improvement.

Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection.

Intensive rice breeding over the past 50 y has dramatically increased productivity especially in the indica subspecies, but our knowledge of the genomic changes associated with such improvement has been limited. In this study, we analyzed low-coverage sequencing data of 1,479 rice accessions from 73 countries, including landraces and modern cultivars. We identified two major subpopulations, indica I (IndI) and indica II (IndII), in the indica subspecies, which corresponded to the two putative heterotic groups resulting from independent breeding efforts. We detected 200 regions spanning 7.8% of the rice genome that had been differentially selected between IndI and IndII, and thus referred to as breeding signatures. These regions included large numbers of known functional genes and loci associated with important agronomic traits revealed by genome-wide association studies. Grain yield was positively correlated with the number of breeding signatures in a variety, suggesting that the number of breeding signatures in a line may be useful for predicting agronomic potential and the selected loci may provide targets for rice improvement.

RiceVarMap: a comprehensive database of rice genomic variations.

Rice Variation Map (RiceVarMap, http:/ricevarmap.ncpgr.cn) is a database of rice genomic variations. The database provides comprehensive information of 6,551,358 single nucleotide polymorphisms (SNPs) and 1,214,627 insertions/deletions (INDELs) identified from sequencing data of 1479 rice accessions. The SNP genotypes of all accessions were imputed and evaluated, resulting in an overall missing data rate of 0.42% and an estimated accuracy greater than 99%. The SNP/INDEL genotypes of all accessions are available for online query and download. Users can search SNPs/INDELs by identifiers of the SNPs/INDELs, genomic regions, gene identifiers and keywords of gene annotation. Allele frequencies within various subpopulations and the effects of the variation that may alter the protein sequence of a gene are also listed for each SNP/INDEL. The database also provides geographical details and phenotype images for various rice accessions. In particular, the database provides tools to construct haplotype networks and design PCR-primers by taking into account surrounding known genomic variations. These data and tools are highly useful for exploring genetic variations and evolution studies of rice and other species.

Prevalence and risk factors for Helicobacter pylori infection in southwest China: a study of health examination participants based on 13C-urea breath test

Background/aim: Helicobacter pylori (H. pylori) has a high prevalence in developing countries. We aimed to investigate the current prevalence of H. pylori, as well as its potential serum risk factors, in a population from southwest China.Materials and methods: This cross-sectional study included 10,912 subjects who received medical examinations at the First Affiliated Hospital of Chongqing Medical University in 2014. Data regarding physical examinations and biochemical measurements were collected, and H. pylori infection was diagnosed with a 13C-urea breath test. Logistic regression was conducted to identify the risk factors for H. pylori infection.Results: The infection rate of H. pylori was 34.4% (3750/10,912). Older age, lower albumin levels, and higher total cholesterol, LDL-cholesterol, and fasting blood sugar were significantly associated with increased incidence of H. pylori infection. Moreover, logistic regression analysis showed that older age, low albumin, and hyperglycemia were independent risk factors for H. pylori infection after adjusting for other covariables.Conclusion: The results from our study showed that H. pylori was prevalent in southwest China. Older age, low albumin levels, and hyperglycemia were significant risk factors associated with H. pylori infection.