Biology subjects

Zeng, P.

Publications and source records attributed to Zeng, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Functional and evolutionary insights into the emerging tet(X4)-carrying non-O1/O139 Vibrio cholerae from retail pork

The tigecycline resistance gene tet(X4) is prevalent in Enterobacteriaceae, particularly in Escherichia coli. To our knowledge, no study has reported the dissemination dynamics of tet(X4) in Vibrio spp. Herein, we isolated and characterized a first tet(X4)-positive non-O1/O139 Vibrio cholerae isolate from retail pork. Genomic sequencing identified a novel tet(X4) variant in the V. cholerae chromosome, harboring a G568A nucleotide substitution that resulted in an Ala190Thr (A190T) amino acid substitution in Tet(X4). While this Tet(X4)-A190T variant conferred lower phenotypic resistance to tetracyclines (including tigecycline) than the wild-type Tet(X4), its overall catalytic efficiency against these antibiotics was paradoxically enhanced despite a reduced substrate affinity. Genomic comparisons revealed that two copies of ISCR2 flanked the variant gene, and the structure was ISCR2-hp-hp-abh-tet(X4)G568A -ISCR2, which is highly homologous to the reported E. coli plasmids carrying tet(X4). In addition, it confirmed the presence of an ISCR2-mediated circular intermediate, proving this modules capacity for horizontal transfer of the tet(X4)G568A variant. Furthermore, the ISCR2-tet(X4) genetic structure carrying the G568A substitution was integrated within a chimeric SXT/R391-like integrative and conjugative element (ICE), which is also serving as a vehicle for genetic dissemination. As per our knowledge, this is the first report on the emergence of SXT/R391-like ICE carrying tet(X4) in Vibrio strains. Our finding demonstrates that the clinically relevant tigecycline resistance gene tet(X4), previously confined mainly to Enterobacterales from humans and livestock, is now actively spreading into environmental Vibrio populations. This cross-species transfer highlights a previously underappreciated ecological and public health concern in aquatic ecosystems.

microbiology↗

bioRxiv2026

ExMODE: A Multi-Omics Repository for Extremophile Adaptation and Bioprospecting

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

microbiology↗

bioRxiv2026

MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes

Drugs induce coordinated phenotypic changes across multiple modalities, including transcriptional reprogramming and cellular morphological remodeling. Predicting these drug-induced modality changes is central to drug discovery, mechanism-of-action studies and precision therapeutics, however, prediction performance depends critically on how both drug compounds and cellular states are represented. Despite rapid advances in drug molecular and gene representation methods, a systematic evaluation of these methods remains lacking. Herein, we introduce MVCBench, a comprehensive benchmarking framework for evaluating drug molecular and gene representation methods in predicting drug-induced multimodal virtual cell (MVC) phenotypes. MVCBench leverages large-scale transcriptomic and high-content imaging data and systematically evaluates 24 representation methods (12 drug molecular and 12 gene representation methods) across nearly 1.1 million drug-induced profiles, under both in-distribution and out-of-distribution settings spanning unseen compounds, cell lines, assay plates and datasets. Our benchmarking reveals a pronounced modality-dependent asymmetry: advanced drug molecular representations substantially improve the prediction of drug-induced morphological phenotypes but provide only limited gains for gene expression prediction relative to classical fingerprints, whereas task-specific gene representations outperform general-purpose foundation models in predicting drug-induced transcriptomic responses. Predictive performance also deteriorates sharply under distribution shift, highlighting persistent challenges in cross-dataset and cross-platform generalization. We further show that integrating transcriptomic and morphological modalities consistently improves prediction accuracy, and derive practical design principles for MVC architectures, including modality-aware loss calibration and fusion strategies. Together, MVCBench provides a systematic foundation for evaluating representation methods and offers guidance for developing robust MVC models of drug-induced cellular responses.

bioinformatics↗

bioRxiv2025

GALA: A Unified Landmark-Free Framework for Coarse-to-Fine Spatial Alignment Across Resolutions and Modalities in Spatial Transcriptomics

Spatial transcriptomics alignment is challenged by technical variations, including geometric distortions from tissue preparation and platform-driven differences in resolution and modality. These issues create diverse alignment scenarios, from matched and mismatched resolutions to cross-modality integration, while partial tissue coverage further complicates the task. To overcome these limitations, we introduce GALA (Genetic Algorithm-guided Large Deformation Alignment), a unified, landmark-free framework that couples global affine transformation and local diffeomorphic deformation within a single optimisation. Its modality-aware rasterisation harmonises transcriptomic and histological data into a shared grid, enabling landmark-free, multimodal alignment across resolutions and modalities. Evaluated on diverse human and mouse datasets, GALA outperforms existing methods in accuracy, computational efficiency, and biological interpretability for both complete and partial tissue alignment.

genomics↗

bioRxiv2024

Structural basis for the inhibition of coronaviral main proteases by PF-00835231

The main protease (Mpro) of coronaviruses plays a key role in viral replication, thus serving as a hot target for drug design. It has been proven that PF-00835231 is promising inhibitor of SARS-CoV-2 Mpro. Here, we report the inhibition potency of PF-00835231 against SARS-CoV-2 Mpro and seven Mpro mutants (G15S, M49I, Y54C, K90R, P132H, S46F, and V186F) from SARS-CoV-2 variants. The results confirm that PF-00835231 has broad-spectrum inhibition against various coronaviral Mpros. In addition, the crystal structures of SARS-CoV-2 Mpro, SARS-CoV Mpro, MERS-CoV Mpro, and seven SARS-CoV-2 Mpro mutants (G15S, M49I, Y54C, K90R, P132H, S46F, and V186F) in complex with PF-00835231 are solved. A detailed analysis of these structures reveal key determinants essential for inhibition and elucidates the binding modes of different coronaviral Mpros. Given the importance of the main protease for the treatment of coronaviral infection, structural insights into the Mpro inhibition by PF-00835231 can accelerate the design of novel antivirals with broad-spectrum efficacy against different human coronaviruses.

molecular biology↗

bioRxiv2021

Structural basis of main proteases of coronavirus bound to drug candidate PF-07321332

The high mutation rate of COVID-19 and the prevalence of multiple variants strongly support the need for pharmacological options to complement vaccine strategies. One region that appears highly conserved among different genus of coronaviruses is the substrate binding site of the main protease (Mpro or 3CLpro), making it an attractive target for the development of broad-spectrum drugs for multiple coronaviruses. PF-07321332 developed by Pfizer is the first orally administered inhibitor targeting the main protease of SARS-CoV-2, which also has shown potency against other coronaviruses. Here we report three crystal structures of main protease of SARS-CoV-2, SARS-CoV and MERS-CoV bound to the inhibitor PF-07321332. The structures reveal a ligand-binding site that is conserved among SARS-CoV-2, SARS-CoV and MERS-CoV, providing insights into the mechanism of inhibition of viral replication. The long and narrow cavity in the cleft between domains I and II of main protease harbors multiple inhibitor binding sites, where PF-07321332 occupies subsites S1, S2 and S4 and appears more restricted compared with other inhibitors. A detailed analysis of these structures illuminated key structural determinants essential for inhibition and elucidated the binding mode of action of main proteases from different coronaviruses. Given the importance of main protease for the treatment of SARS-CoV-2 infection, insights derived from this study should accelerate the design of safer and more effective antivirals.

biophysics↗

bioRxiv2021

TaBln1 negatively regulates wheat resistance to stripe rust by reducing Ca2+ influx

Blufensin1 (Bln1) has been identified as a negative regulator of basal defense mechanisms that is unique to the cereal grain crops barley, wheat, and rice. However, the molecular mechanisms through which Blufensin1 regulates the wheat immune response are poorly understood. In this study, we found that TaBln1 is significantly induced by Puccinia striiformis f. sp. tritici (Pst) virulent race CYR31 infection. Knockdown the expression of TaBln1 by virus-induced gene silencing reduced Pst growth and development, and enhanced the host defense response. In addition, TaBln1 was found to physically interact with TaCaM3 on the plasma membrane. Silencing TaCaM3 with virus-induced gene silencing increased fungal infection areas and sporulation and reduced wheat resistance to the Pst CYR23 and CYR31. Moreover, we found that the TaCaM3 transcription level could be induced by treatment with chitin but not flg22. Silencing TaCaM3 decreased the Ca2+ influx induced by chitin, but silencing TaBln1 increased the Ca2+ influx in vivo using a non-invasive micro-test technique. Taken together, we identified the wheat negative regulator TaBln1, which interacts with TaCaM3 to impair Ca2+ influx and inhibits plant defenses. One-sentence summaryTaBln1 negatively regulate wheat resistance to stripe rust possibly due to the interaction with TaCaM3 on the plasma membrane, which impairs the calcium influx modulated by TaCaM3.

pathology↗

bioRxiv2019

Glutamatergic and GABAergic neuronal populations in thedorsolateral Periacqueductual Gray have differentfunctional roles in fear conditioning

It is though that only a subset of brain structures can encode emotional states. This can be investigated though a set of properties, including the ability of neurons to respond to a conditioned stimulus (CS) preceding an aversive unconditioned stimulus (US). The dorsolateral periacqueductal gray (dPAG) is a midbrain structure though to have an essential role in coordinating defensive behaviors in response to aversive stimulation. But its ability of dPAG neurons to encode a CS following fear conditioning as not been sufficiently studied.\n\nHere we used calcium imaging by fiber photometry to record the activity of dPAGVGluT2+ and dPAGGAD2+ neuronal populations during unconditioned and conditioned aversive stimulation. Then, following an unconditioned stimulation we performed a retrieval experiment to quantify memory-like responses of dPAG neurons. This shown that whilst both dPAGVGluT2+ and dPAGGAD2+ neuronal populations respond to direct US stimulation, and to CS stimulation during conditioning, only the dPAGVGluT2+ population persisted in responding to the CS stimulation during retrieval. Finally, to better understand dPAGVGluT2+ and dPAGGAD2+ connectivity patterns, we performed a cell specific monosynaptic retrograde rabies virus tracing experiment. This revealed that different patterns of fibers projects to dPAGVGluT2+ and dPAGGAD2+, further complementing our recording showing divergences between PAGVGluT2+ and dPAGGAD2+ populations.

neuroscience↗

bioRxiv2019

Relationship between birth weight and chronic kidney disease: an integrative analysis of observational studies and causal inference through genetic approaches

ObjectiveAlthough many observational studies have shown that there was an inverse association between birth weight and chronic kidney disease (CKD) in adults, whether such association is causal remains largely unclear.\n\nMethodsWe first conducted a systematic review and meta-analysis to investigate the association between birth weight and CKD. Then using a set of valid instrumental variables for birth weight, we performed a two-sample Mendelian randomization (MR) to evaluate its causal effect on CKD based on summary association statistics available from large scale genome-wide association study (GWAS) (up to 143,677 individuals for birth weight and 118,147 individuals for CKD). We further validated the MR results with extensive sensitive analyses.\n\nResultsThe results of meta-analysis showed that individuals with low birth weight have about 76% (95% CI 36[~]126%) higher risk of CKD in late life compared with those with normal birth weight. Depending on 26 instrumental variables, the inverse variance weighted MR showed that the odds ratio per one SD increase of birth weight on CKD was estimated to be 0.91 (95% CI 0.72[~]1.14, p=0.396). The similar null association between birth weight and CKD is also observed using the weighted median method and maximum likelihood method as well as the Egger regression. Such non-significant association is robust against potential instrumental outliers and pleiotropic effects.\n\nConclusionOur study identifies an inverse association between birth weight and adult CKD in observational studies, while it is not supportive of the causal role of birth weight on CKD based on our MR analysis.

genetics↗

bioRxiv2019

Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

Integrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

genetics↗

bioRxiv2019

Association between premorbid body mass index and amyotrophic lateral sclerosis: causal inference through genetic approaches

Background: Inverse association between premorbid body mass index (BMI) and amyotrophic lateral sclerosis (ALS) has been discovered in observational studies; however, whether this association is causal remains largely unknown. Methods: We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of genetically increased BMI with the risk of ALS. The analyses were implemented using summary statistics obtained for the independent instruments identified from large-scale genome-wide association studies of BMI (up to ~770,000 individuals) and ALS (up to ~81,000 individuals). The causal relationship between BMI and ALS was estimated using inverse-variance weighted methods and was further validated through extensive complementary and sensitivity analyses. Findings: Using 1,031 instruments strongly related to BMI, the causal effect of per one standard deviation increase of BMI was estimated to be 1.04 (95% CI 0.97~1.11, p=0.275) in the European population. The null association between BMI and ALS discovered in the European population also held in the East Asian population and was robust against various modeling assumptions and outlier biases. Additionally, the Egger-regression and MR-PRESSO ruled out the possibility of horizontal pleiotropic effects of instruments. Interpretation: Our results do not support the causal role of genetically increased or decreased BMI on the risk of ALS.

neuroscience↗

bioRxiv2018

Birth weight is not causally associated with adult asthma: results from instrumental variable analyses

The association between lower birth weight and childhood asthma is well established by observational studies. However, it remains unclear whether the influence of lower birth weight on asthma can persist into adulthood. Here, we conducted a Mendelian randomization analysis to assess the causal relationship of birth weight on the risk of adult asthma. Specifically, we carefully selected genetic instruments based on summary statistics obtained from large-scale genome-wide association meta-analyses of birth weight (up to ~160,000 individuals) and adult asthma (up to ~62,000 individuals). We performed Mendelian randomization using two separate approaches: a genetic risk score approach and a two-sample inverse-variance weighted (IVW) approach. With 37 genetic instruments for birth weight, we estimated the causal effect per one standard deviation (SD) change of birth weight to be an odds ratio (OR) of 1.00 (95% CI 0.98~1.03, p=0.737) using the genetic risk score method. We did not observe nonlinear relationship or gender difference for the estimated causal effect. In addition, with the IVW method, we estimated the causal effect of birth weight on adult asthma was observed (OR=1.02, 95% CI 0.84~1.24, p=0.813). Additionally, the iMAP method provides no additional genome-wide evidence supporting the causal effects of birth weight on adult asthma. The result of the IVW method was robust against various sensitivity analyses, and MR-PRESSO and the Egger regression showed that no instrument outliers and no horizontal pleiotropy were likely to bias the results. Overall, this Mendelian randomization study provides no evidence for the fetal origins of diseases hypothesis for adult asthma, implying that the impact of birth weight on asthma in years of children and adolescents does not persist into adult and previous findings may be biased by confounders.

epidemiology↗

bioRxiv2018

Noninvasive prenatal detection of fetal trisomy and single gene disease by shotgun sequencing of placenta originated exosome DNA: a proof-of-concept validation

BackgroundDuring human pregnancy, Placental trophectoderm cells can release exosomes into maternal circulation. Trophoblast cells also give rise to cell-free DNA (cfDNA) and has been used for noninvasive prenatal screening for chromosomal aneuploidy. We intended to prove the existence of exosomal DNA (exoDNA) in the exosomes of maternal blood and compared exoDNA with plasma cfDNA in terms of genome distribution, fragment length, and the possibility of detecting genetic diseases.\n\nMethodsMaternal blood from 20 euploid pregnancies, 9 T21 pregnancies, 3 T18 pregnancies, 1 T13 pregnancy and 2 pregnancies with FGFR3 mutations were obtained. Exosomes enriched from maternal plasma were confirmed by transmission electronic microscopy (TEM), western blotting and flow cytometry. ExoDNA was extracted and its fetal origin was confirmed by realtime fluorescence quantitative PCR(Q-PCR). Besides, exoDNA content was uncovered by Q-PCR. To characterize exoDNA and compare with cfDNA, pair-end whole genome sequencing was performed. Lastly, the fetal risk of genetic diseases was analyzed using the exoDNA sequencing data.\n\nResultsExoDNA span on all 23 pairs of chromosomes and mitochondria, sharing a similar distribution pattern and higher GC content comparing with cfDNA. ExoDNA showed shorter fragments yet lower fetal fraction than cfDNA. ExoDNA could be used to determine fetal gender correctly, and all trisomies as well as de novo FGFR3 mutations.\n\nConclusionsWe proved that fetal exoDNA could be identified in the exosomes extracted from maternal plasma. ExoDNA shared some similar features to cfDNA and could potentially be used to detect genetic diseases in fetus.

genetics↗

bioRxiv2018

Characterization of glutamatergic VTA neural population responses to aversive and rewarding conditioning in freely-moving mice

The Ventral Tegmental Area (VTA) is a midbrain structure known to integrate aversive and rewarding stimuli, a function involving VTA Dopaminergic and GABAergic neurons. VTA also contains a less known population: glutamatergic (VGluT2) neurons. Direct activation of VGluT2 soma evokes rewarding behaviors, while stimulation of their axonal projections to the Nucleus Accumbens (NAc) and the Lateral Habenula (LHb) evokes aversive behaviors. Here, a systematic investigation of the VTAVGluT2+ population response to aversive or rewarding conditioning facilitated our understanding these conflicting properties. We recorded calcium signals from VTA glutamatergic population neurons using fiber photometry in VGluT2-cre mice to investigate how the VTA glutamatergic neuronal population was recruited by aversive and rewarding stimulation, both during unconditioned and conditioned protocols. Our results revealed that, as a population, VTAVGluT2+ neurons responded similarly to unconditioned-aversive and unconditioned-rewarding stimulation. During aversive and rewarding conditioning, the CS-evoked responses gradually increased across trials whilst the US-evoked response remained stable. Retrieval 24 h after conditioning, during which mice received only CS presentation, resulted in VTAVGluT2+ neurons strongly responding to CS presentation and to the expected-US but only for aversive conditioning. The inputs and outputs of VTAVGluT2+ neurons were then investigated using Cholera Toxin B (CTB) and rabies virus, and we propose based on all results that VTAVGluT2+ neurons specialized function may be partially due to their connectivity.

neuroscience↗

bioRxiv2018

Causal Association between Birth Weight and Adult Diseases: Evidence from a Mendelian Randomisation Analysis

BackgroundIt has long been hypothesized that birth weight has a profound long-term impact on individual predisposition to various diseases at adulthood: a hypothesis commonly referred to as the fetal origins of adult diseases. However, it is not fully clear to what extent the fetal origins of adult diseases hypothesis holds and it is also not completely known what types of adult diseases are causally affected by birth weight. Determining the causal impact of birth weight on various adult diseases through traditional randomised intervention studies is a challenging task.\n\nMethodsMendelian randomisation was employed and multiple genetic variants associated with birth weight were used as instruments to explore the relationship between 21 adult diseases and 38 other complex traits from 37 large-scale genome-wide association studies up to 340,000 individuals of European ancestry. Causal effects of birth weight were estimated using inverse-variance weighted methods. The identified causal relationships between birth weight and adult diseases were further validated through extensive sensitivity analyses and simulations.\n\nResultsAmong the 21 adult diseases, three were identified to be inversely causally affected by birth weight with a statistical significance level passing the Bonferroni corrected significance threshold. The measurement unit of birth weight was defined as its standard deviation (i.e. 488 grams), and one unit lower birth weight was causally related to an increased risk of coronary artery disease (CAD), myocardial infarction (MI), type 2 diabetes (T2D) and BMI-adjusted T2D, with the estimated odds ratios of 1.34 [95% confidence interval (CI) 1.17 - 1.53, p = 1.54E-5], 1.30 (95% CI 1.13 - 1.51, p = 3.31E-4), 1.41 (95% CI 1.15 - 1.73, p = 1.11E-3) and 1.54 (95% CI 1.25 - 1.89, p = 6.07E-5), respectively. All these identified causal associations were robust across various sensitivity analyses that guard against various confounding due to pleiotropy or maternal effects as well as inverse causation. In addition, analysis on 38 additional complex traits found that the inverse causal association between birth weight and CAD/MI/T2D was not likely to be mediated by other risk factors such as blood-pressure related traits and adult weight.\n\nConclusionsThe results suggest that lower birth weight is causally associated with an increased risk of CAD, MI and T2D in later life, supporting the fetal origins of adult diseases hypothesis.

epidemiology↗

bioRxiv2018

Causal Effects of Blood Lipids on Amyotrophic Lateral Sclerosis: A Mendelian Randomization Study

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disorder that is predicted to increase across the globe by ~70% in the following decades. Understanding the disease causal mechanism underlying ALS and identifying modifiable risks factors for ALS hold the key for the development of effective preventative and treatment strategies. Here, we investigate the causal effects of four blood lipid traits that include high density lipoprotein (HDL), low density lipoprotein (LDL), total cholesterol (TC), and triglycerides (TG) on the risk of ALS. By leveraging instrument variables from multiple large-scale genome-wide association studies in both European and East Asian populations, we carry out one of the largest and most comprehensive Mendelian randomization analyses performed to date on the causal relationship between lipids and ALS. Among the four lipids, we found that only LDL is causally associated with ALS and that higher LDL level increases the risk of ALS in both the European and East Asian populations. Specifically, the odds ratio of ALS per one standard deviation (i.e. 39.0 mg/dL) increase of LDL is estimated to be 1.14 (95% CI 1.05 - 1.24, p = 1.38E-3) in the European and population and 1.06 (95% CI 1.00 - 1.12, p = 0.044) in the East Asian population. The identified causal relationship between LDL and ALS is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our study provides important evidence supporting the causal role of higher LDL on increasing the risk of ALS, paving ways for the development of preventative strategies for reducing the disease burden of ALS across multiple nations.

epidemiology↗

bioRxiv2018

Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information

MotivationIn the past few years many novel prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation. However, those approaches typically ignore in model fitting the important group structures or functional classifications that naturally exists in genetic data.\n\nMethodsIn the present study, we applied a novel model averaging approach, called Jackknife Model Averaging Prediction (JMAP), for high dimensional genetic risk prediction while incorporating KEGG pathway information into the model specification. JMAP selects the optimal weights across candidate models by minimizing a cross-validation criterion in a jackknife way. Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one. We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods. We finally applied JMAP to five real cancer datasets that are publicly available from TCGA.\n\nResultsThe simulations showed that, compared with other existing approaches, JMAP performed best or are among the best methods across a range of scenarios. For example, among 14 out of 16 simulation settings with PVE=0.3, JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso. We further found that in the simulation the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude. In the real data application, JMAP also behaves comparably or better compared with the other methods for both continuous and binary phenotypes. For example, for the COAD, CRC and PAAD data sets, the average gains of predictive accuracy of JMAP are 0.019, 0.064 and 0.052 compared with gsslasso.\n\nConclusionThe proposed method JMAP is a novel method that can provide more accurate phenotypic prediction while incorporating external useful group information.

genomics↗

bioRxiv2018

Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Pe-nalized Gaussian Mixture Models

MotivationGenome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits - a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.\n\nResultsWe develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP both in terms of high association mapping power and in terms of accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. iMAP is freely available at www.xzlab.org/software.html.

bioinformatics↗