A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies.

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.

An R-R-type MYB transcription factor promotes non-climacteric pepper fruit carotenoid pigment biosynthesis.

Carotenoids are major accessory pigments in the chloroplast, and they also act as phytohormones and volatile compound precursors to influence plant development and confer characteristic colours, affecting both the aesthetic and nutritional value of fruits. Carotenoid pigmentation in ripening fruits is highly dependent on developmental trajectories. Transcription factors incorporate developmental and phytohormone signalling to regulate the biosynthesis process. By contrast to the well-established pathways regulating ripening-related carotenoid biosynthesis in climacteric fruit, carotenoid regulation in non-climacteric fruit is poorly understood. Capsanthin is the primary carotenoid of non-climacteric pepper (Capsicum) fruit; its biosynthesis is tightly associated with fruit ripening, and it confers red pigmentation to the ripening fruit. In the present study, using a coexpression analysis, we identified an R-R-type MYB transcription factor, DIVARICATA1, and demonstrated its role in capsanthin biosynthesis. DIVARICATA1 encodes a nucleus-localised protein that functions primarily as a transcriptional activator. Functional analyses showed that DIVARICATA1 positively regulates carotenoid biosynthetic gene (CBG) transcript levels and capsanthin levels by directly binding to and activating CBG promoter transcription. Furthermore, an association analysis revealed a significant positive association between DIVARICATA1 transcription level and capsanthin content. ABA promotes capsanthin biosynthesis in a DIVARICATA1-dependent manner. Comparative transcriptomic analysis of DIVARICATA1 in Solanaceae plants showed that its function likely differs among species. Moreover, the pepper DIVARICATA1 gene could be regulated by the ripening regulator MADS-RIN. The present study illustrates the transcriptional regulation of capsanthin biosynthesis and offers a target for breeding peppers with high red colour intensity.

Bupleurum exerts antiarrhythmic effects by inhibiting L-type calcium channels in mouse ventricular myocytes.

BACKGROUND: Bupleurum (Bup), is a traditional effective medicine to treat colds and fevers in clinics. Multiple studies have demonstrated that Bup exhibites various biological activities, including cardioprotective effects, anti-inflammatory, anticancer, antipyretic, antimicrobial, and antiviral effects, etc. Currently, the effects of Bup on cardiac electrophysiology have not been reported yet. METHODS: Electrocardiogram recordings were used to investigate the effects of Bup on aconitine-induced arrhythmias. Patch-clamp techniques were used to explore the effects of Bup on APs and ion currents. RESULTS: Bup reduced the incidence of ventricular fibrillation (VF) and delayed the onset time of ventricular tachycardia (VT) in mice. Additionally, Bup (40 mg/mL) suppressed DADs induced by high-Ca2+ and shortened action potential duration at 50 % completion of repolarization (APD50) and action potential duration at 90 % completion of repolarization (APD90) to 60.89 % ± 8.40 % and 68.94 % ± 3.24 % of the control, respectively. Moreover, Bup inhibited L-type calcium currents (ICa.L) in a dose-dependent manner, with an IC50 value of 25.36 mg/mL. Furthermore, Bup affected the gated kinetics of L-type calcium channels by slowing down steady-state activation, accelerating the steady-state inactivation, and delaying the inactivation-recovery process. However, Bup had no effects on the Transient sodium current (INa.T), ATX II-increased late sodium current (INa.L), transient outward current (Ito), delayed rectifier potassium current (IK), or inward rectifier potassium current (IK1). CONCLUSION: Bup is an antiarrhythmic agent that may exert its antiarrhythmic effects by inhibiting L-type calcium channels.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.

MOTIVATION: Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. RESULTS: We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. AVAILABILITY AND IMPLEMENTATION: https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.

The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma.

OBJECTIVE: To evaluate whether quantification of lung GGN shape is useful in predicting pathological categorization of lung adenocarcinoma and guiding the clinic. METHODS: 98 patients with primary lung adenocarcinoma were pathologically confirmed and CT was performed preoperatively, and all lesions were pathologically ≤ 30 mm in size. On CT images, we measured the maximum area of the lesion's cross-section (MA). The longest diameter of the tumor (LD) was marked with points A and B, and the perpendicular diameter (PD) was marked with points C and D, which was the longest diameter perpendicular to AB. and D, which was the longest diameter perpendicular to AB. We took angles A and B as big angle A (BiA) and small angle A (SmA). We measured the MA, LD, and PD, and for analysis we derived the LD/PD ratio and the BiA/SmA ratio. The data were analysed using the chi-square test, t-test, ROC analysis, and binary logistic regression analysis. RESULTS: Precursor glandular lesions (PGL) and microinvasive adenocarcinoma (MIA) were distinguished from invasive adenocarcinoma (IAC) by the BiA/SmA ratio and LD, two independent factors (p = 0.007, p = 0.018). Lung adenocarcinoma pathological categorization was indicated by the BiA/SmA ratio of 1.35 and the LD of 11.56 mm with sensitivity of 81.36% and 71.79%, respectively; specificity of 71.79% and 74.36%, respectively; and AUC of 0.8357 (95% CI: 0.7558-0.9157, p < 0.001), 0.8666 (95% CI: 0.7866-0.9465, p < 0.001), respectively. In predicting the pathological categorization of lung adenocarcinoma, the area under the ROC curve of the BiA/SmA ratio combined with LD was 0.9231 (95% CI: 0.8700-0.9762, p < 0.001), with a sensitivity of 81.36% and a specificity of 89.74%. CONCLUSIONS: Quantification of lung GGN morphology by the BiA/SmA ratio combined with LD could be helpful in predicting pathological classification of lung adenocarcinoma.

Sepsis as a Potential Risk Factor for Upper Gastrointestinal Bleeding in Critically Ill Patients: A Systematic Review and Meta-analysis.

Purpose: Sepsis is a common and critical condition in intensive care units (ICUs) known to complicate patient outcomes. Previous studies have indicated an association between sepsis and various ICU morbidities, including upper gastrointestinal bleeding (UGIB). However, the extent of this relationship and its implications in ICU settings remain inadequately quantified. This study aims to elucidate the association between sepsis and the risk of UGIB in ICU patients. Methods: A comprehensive meta-analysis was conducted, encompassing nine studies with a total of nearly 9000 participants. These studies reported events for both sepsis and nonsepsis patients separately. Pooled odds ratios (ORs) were calculated to assess the risk of UGIB in septic versus nonseptic ICU patients. Subgroup analyses were conducted based on age and study design, and both unadjusted and adjusted ORs were examined. Results: The pooled OR indicated a significant association between sepsis and UGIB (OR = 3.276, 95% CI: 1.931 to 5.557). Moderate heterogeneity was observed (I² = 43.9%). The association was significant in adults (pooled OR = 4.083) but not in children. No difference in association was found based on the study design. Unadjusted and adjusted ORs differed slightly, indicating the influence of confounding factors. Conclusion: This meta-analysis reveals a significant association between sepsis and an increased risk of UGIB in ICU patients, particularly in adults. These findings highlight the need for vigilant monitoring and proactive management of septic ICU patients to mitigate the risk of UGIB. Future research should focus on understanding the underlying mechanisms and developing tailored preventive strategies.

Control for population stratification in genetic association studies based on GWAS summary statistics.

Over the past years, genome-wide association studies (GWAS) have generated a wealth of new information. Summary data from many GWAS are now publicly available, promoting the development of many statistical methods for association studies based on GWAS summary statistics, which avoids the increasing challenges associated with individual-level genotype and phenotype data sharing. However, for population-based association studies such as GWAS, it has been long recognized that population stratification can seriously confound association results. For large GWAS, it is very likely that there exist population stratification and cryptic relatedness, which will result in inflated Type I error in association testing. Although many methods have been developed to control for population stratification, only two of these approaches can be used to control population stratification without individual-level data: one is based on genomic control (GC) and the other one is based on linkage disequilibrium score regression (LDSC). However, the performance of these two approaches is currently unknown. In this study, we use extensive simulation studies including populations with subpopulations, spatially structured populations, and populations with cryptic relatedness to compare the performance of these two approaches to control for population stratification using only GWAS summary statistics without individual-level data. Data sets from the genetic analysis workshop 19 and UK Biobank are also used to evaluate these two approaches. We demonstrate that the intercept of LDSC can be used as a more accurate correction factor than GC. The results from this study will provide very useful information for researchers using GWAS summary statistics while trying to control for population stratification.

A CT Radiomics-Based Risk Score for Preoperative Estimation of Intraoperative Superior Mesenteric-Portal Vein Involvement in Pancreatic Ductal Adenocarcinoma.

BACKGROUND: The current radiologic criteria for assessing intraoperative superior mesenteric-portal vein (SMPV) involvement (i.e., presence of tumor-SMPV contact >180° or venous deformity) in pancreatic ductal adenocarcinoma (PDAC) are highly specific but insufficiently sensitive. Therefore, development of improved markers for a more accurate prediction is essential. This study aimed to develop a risk score model to estimate SMPV involvement in PDAC using radiomics analysis of computed tomography (CT) images. METHODS: Data from two institution-based cohorts of PDAC patients undergoing preoperative CT scans were used to develop (n = 173) and validate (n = 156) a radiomics-based risk score of SMPV involvement using clinical and imaging variables. A radiomics signature was developed based on 2436 radiomic features extracted from the semi-automatic three-dimensional segmentation ofn CT images. The SMPV involvement risk score was built using multivariate logistic regression and compared with the current radiologic criteria. RESULTS: The study surgically identified SMPV involvement in 59 (34.1%) and 57(36.5 %) patients with PDAC in the development and validation cohorts, respectively. A 12-feature-based radiomics signature achieved areas under receiver operating characteristics curves (AUCs) of 0.89 or greater for estimating SMPV involvement. Multivariate regression identified the radiomics signature and SMPV deformity as associated with SMPV involvement. The risk score model had significantly improved AUC (0.928 vs. 0.768; P < 0.001) and sensitivity (84.2% vs. 66.7%; P = 0.025) in the radiologic evaluation. CONCLUSIONS: The novel risk score in this study, combining radiomics signature and venous deformity, demonstrated promising performance for estimating SMPV involvement preoperatively for patients with PDAC.

Nomogram Estimating Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma From Preoperative Gadoxetate Disodium-Enhanced MRI.

BACKGROUND: Vessels encapsulating tumor clusters (VETC) pattern is a novel microvascular pattern associated with poor outcomes of hepatocellular carcinoma (HCC). Preoperative estimation of VETC has potential to improve treatment decisions. PURPOSE: To develop and validate a nomogram based on gadoxetate disodium-enhanced MRI for estimating VETC in HCC and to evaluate whether the estimations are associated with recurrence after hepatic resection. STUDY TYPE: Retrospective. POPULATION: A total of 320 patients with HCC and histopathologic VETC pattern assessment from three centers (development cohort:validation cohort = 173:147). FIELD STRENGTH/SEQUENCE: A3.0  T/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, and 3D T1-weighted gradient-echo sequences. ASSESSMENT: A set of previously reported VETC- and/or prognosis-correlated qualitative and quantitative imaging features were assessed. Clinical and imaging variables were compared based on histopathologic VETC status to investigate factors indicating VETC pattern. A regression-based nomogram was then constructed using the significant factors for VETC pattern. The nomogram-estimated VETC stratification was assessed for its association with recurrence. STATISTICAL TESTS: Fisher exact test, t-test or Mann-Whitney test, logistic regression analyses, Harrell's concordance index (C-index), nomogram, Kaplan-Meier curves and log-rank tests. P value < 0.05 was considered statistically significant. RESULTS: Pathological VETC pattern presence was identified in 156 patients (development cohort:validation cohort = 83:73). Tumor size, presence of heterogeneous enhancement with septations or with irregular ring-like structures, and necrosis were significant factors for estimating VETC pattern. The nomogram incorporating these indicators showed good discrimination with a C-index of 0.870 (development cohort) and 0.862 (validation cohort). Significant differences in recurrence rates between the nomogram-estimated high-risk VETC group and low-risk VETC group were found (2-year recurrence rates, 50.7% vs. 30.3% and 49.6% vs. 31.8% in the development and validation cohorts, respectively). DATA CONCLUSION: The nomogram integrating gadoxetate disodium-enhanced MRI features was associated with VETC pattern preoperatively and with postoperative recurrence in patients with HCC. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Naringin exerts antiarrhythmic effects by inhibiting channel currents in mouse cardiomyocytes.

INTRODUCTION: Naringin, a flavonoid extracted from citrus plants, has a variety of biological effects. Studies have shown that increasing the consumption of flavonoid-rich foods can reduce the incidence of cardiac arrhythmia. Naringin has been reported to have beneficial cardiovascular effects and thus can be used to prevent cardiovascular diseases, but the electrophysiological mechanism through which it prevents arrhythmias has not been elucidated. This study was conducted to investigate the effect of naringin on the transmembrane ion channel currents in mouse ventricular myocytes and the antiarrhythmic effect of this compound on Langendorff-perfused mouse hearts. METHODS: Action potentials (APs) and ionic currents were recorded in isolated ventricular myocytes using the whole-cell patch-clamp technique. Anemone toxin II (ATX II) and CaCl2 were used to induce early afterdepolarizations (EADs) and delayed afterdepolarizations (DADs), respectively. Electrocardiogram (ECG) recordings were conducted in Langendorff-perfused mouse hearts with a BL-420F biological signal acquisition and analysis system. RESULTS: At the cellular level, naringin shortened the action potential duration (APD) of ventricular myocytes and decreased the maximum depolarization velocity (Vmax) of APs.Naringin inhibited the L-type calcium current (ICa.L) and ATX II enhanced the late sodium current (INa.L) in a concentration-dependent manner with IC50 values of 508.5 µmol/L (n = 9) and 311.6 µmol/L (n = 10), respectively. In addition, naringin also inhibited the peak sodium current (INa·P) and delayed the rectifier potassium current (IK) and the transient outward potassium current (Ito). Moreover, naringin reduced ATX II-induced APD prolongation and EADs and had a significant inhibitory effect on CaCl2-induced DADs as well. At the organ level, naringin reduced the incidence of ventricular tachycardia (VT) and ventricular fibrillation (VF) induced by ATX II and shortened the duration of both in isolated hearts. CONCLUSION: Naringin can inhibit the occurrence of EADs and DADs at the cellular level; furthermore, it can inhibit INa.L, ICa.L, INa·P, IK, and Ito in ventricular myocytes. Naringin also inhibits arrhythmias induced by ATX II in hearts. By investigating naringin with this electrophysiological method for the first time, we determined that this flavonoid may be a multichannel blocker with antiarrhythmic effects.

MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data.

With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared with methods that only use a single marker or trait, the joint analysis of multiple genetic variants and multiple traits is more powerful since such an analysis can fully incorporate the correlation structure of genetic variants and/or traits and their mutual dependence patterns. However, most of existing methods that simultaneously analyze multiple genetic variants and multiple traits are only applicable to unrelated samples. We develop a new method called MF-TOWmuT to detect association of multiple phenotypes and multiple genetic variants in a genomic region with family samples. MF-TOWmuT is based on an optimally weighted combination of variants. Our method can be applied to both rare and common variants and both qualitative and quantitative traits. Our simulation results show that (1) the type I error of MF-TOWmuT is preserved; (2) MF-TOWmuT outperforms two existing methods such as Multiple Family-based Quasi-Likelihood Score Test and Multivariate Family-based Rare Variant Association Test in terms of power. We also illustrate the usefulness of MF-TOWmuT by analyzing genotypic and phenotipic data from the Genetics of Kidneys in Diabetes study. R program is available at https://github.com/gaochengPRC/MF-TOWmuT.

Effects of rice-prawn (Macrobrachium nipponense) co-culture on the microbial community of soil.

In the Lixiahe region of China, co-culture has been rapidly promoted in flooded paddy fields owing to its ecological and economic benefits. Rice-prawn co-culture can reduce the damage of crab and shrimp to rice growth and paddy field and substantially change the soil microbial community and soil fertility. In this study, we compared changes in the soil microbial community and soil fertility in waterlogged paddies under conventional rice culture (CR), rice-prawn (Macrobrachium nipponense) co-culture (RP), and pond culture (PC). The microbial abundance in RP was significantly higher than that in CR. RP soil microbial diversity was significantly higher than PC soil microbial diversity. The dominant bacteria in RP soil were Proteobacteria, Chloroflexi, and Bacteroidetes. Compared with those in CR, total organic matter (TOM) and total nitrogen in RP were relatively stable, available potassium and available phosphorus (AP) decreased, and other indicators increased significantly. Soil fertility significantly benefited from co-culture, with total organic carbon (TOC) increasing. Interactive relationship analysis showed that TOM, TOC, AP, and NH4+-N were the main factors affecting the microbial community. Co-occurrence network analyses showed that network modularity increased with co-culture, indicating that a unique soil microbial community formed under co-culture, improving the adaptability and tolerance to co-culture. Thus, RP is a suitable culture method for this commercially important species. The results of this study can inform the practical operation of fertilizer use and sustainable development of rice-prawn aquaculture systems. KEY POINTS: • Microbial abundance and diversity increased under rice-prawn co-culture. • Co-culture significantly improved soil fertility, with an increase in TOC. • Rice-prawn co-culture is an ecologically suitable culture method for prawns.

Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering.

Emerging evidence suggests that a genetic variant can affect multiple phenotypes, especially in complex human diseases. Therefore, joint analysis of multiple phenotypes may offer new insights into disease etiology. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes, including the clustering linear combination (CLC) method. Due to the unknown number of clusters for a given data, a simulation procedure must be used to evaluate the p-value of the final test statistic of CLC. This makes the CLC method computationally demanding. In this paper, we use a stopping criterion to determine the number of clusters in the CLC method. We have named our method, hierarchical clustering CLC (HCLC). HCLC has an asymptotic distribution, which is very computationally efficient and makes it applicable for genome-wide association studies. Extensive simulations together with the COPDGene data analysis have been used to assess the type I error rates and power of our proposed method. Our simulation results demonstrate that the type I error rates of HCLC are effectively controlled in different realistic settings. HCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

Genome-wide identification of the Capsicum bHLH transcription factor family: discovery of a candidate regulator involved in the regulation of species-specific bioactive metabolites.

BACKGROUND: The basic helix-loop-helix (bHLH) transcription factors (TFs) serve crucial roles in regulating plant growth and development and typically participate in biological processes by interacting with other TFs. Capsorubin and capsaicinoids are found only in Capsicum, which has high nutritional and economic value. However, whether bHLH family genes regulate capsorubin and capsaicinoid biosynthesis and participate in these processes by interacting with other TFs remains unknown. RESULTS: In this study, a total of 107 CabHLHs were identified from the Capsicum annuum genome. Phylogenetic tree analysis revealed that these CabHLH proteins were classified into 15 groups by comparing the CabHLH proteins with Arabidopsis thaliana bHLH proteins. The analysis showed that the expression profiles of CabHLH009, CabHLH032, CabHLH048, CabHLH095 and CabHLH100 found in clusters C1, C2, and C3 were similar to the profile of carotenoid biosynthesis in pericarp, including zeaxanthin, lutein and capsorubin, whereas the expression profiles of CabHLH007, CabHLH009, CabHLH026, CabHLH063 and CabHLH086 found in clusters L5, L6 and L9 were consistent with the profile of capsaicinoid accumulation in the placenta. Moreover, CabHLH007, CabHLH009, CabHLH026 and CabHLH086 also might be involved in temperature-mediated capsaicinoid biosynthesis. Yeast two-hybrid (Y2H) assays demonstrated that CabHLH007, CabHLH009, CabHLH026, CabHLH063 and CabHLH086 could interact with MYB31, a master regulator of capsaicinoid biosynthesis. CONCLUSIONS: The comprehensive and systematic analysis of CabHLH TFs provides useful information that contributes to further investigation of CabHLHs in carotenoid and capsaicinoid biosynthesis.

A general statistic to test an optimally weighted combination of common and/or rare variants.

Both genome-wide association study and next-generation sequencing data analyses are widely employed to identify disease susceptible common and/or rare genetic variants. Rare variants generally have large effects though they are hard to detect due to their low frequencies. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some adhoc assumptions (e.g., ignoring dependence between rare variants). In this study, we analytically derived optimal weights for both common and rare variants and proposed a general and novel approach to test association between an optimally weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. Additionally, we applied G-TOW and other competitive methods to test disease-associated genes in real data of schizophrenia. The G-TOW has successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic and the sequence kernel association test. Simulation study and real data analysis indicate that G-TOW is a powerful test.

Systematic analysis of the Capsicum ERF transcription factor family: identification of regulatory factors involved in the regulation of species-specific metabolites.

BACKGROUND: ERF transcription factors (TFs) belong to the Apetala2/Ethylene responsive Factor (AP2/ERF) TF family and play a vital role in plant growth and development processes. Capsorubin and capsaicinoids have relatively high economic and nutritional value, and they are specifically found in Capsicum. However, there is little understanding of how ERFs participate in the regulatory networks of capsorubin and capsaicinoids biosynthesis. RESULTS: In this study, a total of 142 ERFs were identified in the Capsicum annuum genome. Subsequent phylogenetic analysis allowed us to divide ERFs into DREB (dehydration responsive element binding proteins) and ERF subfamilies, and further classify them into 11 groups with several subgroups. Expression analysis of biosynthetic pathway genes and CaERFs facilitated the identification of candidate genes related to the regulation of capsorubin and capsaicinoids biosynthesis; the candidates were focused in cluster C9 and cluster C10, as well as cluster L3 and cluster L4, respectively. The expression patterns of CaERF82, CaERF97, CaERF66, CaERF107 and CaERF101, which were found in cluster C9 and cluster C10, were consistent with those of accumulating of carotenoids (ß-carotene, zeaxanthin and capsorubin) in the pericarp. In cluster L3 and cluster L4, the expression patterns of CaERF102, CaERF53, CaERF111 and CaERF92 were similar to those of the accumulating capsaicinoids. Furthermore, CaERF92, CaERF102 and CaERF111 were found to be potentially involved in temperature-mediated capsaicinoids biosynthesis. CONCLUSION: This study will provide an extremely useful foundation for the study of candidate ERFs in the regulation of carotenoids and capsaicinoids biosynthesis in peppers.

A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

SUMMARY: There is an increasing interest in joint analysis of multiple phenotypes for genome-wide association studies (GWASs) based on the following reasons. First, cohorts usually collect multiple phenotypes and complex diseases are usually measured by multiple correlated intermediate phenotypes. Second, jointly analyzing multiple phenotypes may increase statistical power for detecting genetic variants associated with complex diseases. Third, there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. In this paper, we develop a clustering linear combination (CLC) method to jointly analyze multiple phenotypes for GWASs. In the CLC method, we first cluster individual statistics into positively correlated clusters and then, combine the individual statistics linearly within each cluster and combine the between-cluster terms in a quadratic form. CLC is not only robust to different signs of the means of individual statistics, but also reduce the degrees of freedom of the test statistic. We also theoretically prove that if we can cluster the individual statistics correctly, CLC is the most powerful test among all tests with certain quadratic forms. Our simulation results show that CLC is either the most powerful test or has similar power to the most powerful test among the tests we compared, and CLC is much more powerful than other tests when effect sizes align with inferred clusters. We also evaluate the performance of CLC through a real case study. AVAILABILITY AND IMPLEMENTATION: R code for implementing our method is available at http://www.math.mtu.edu/∼shuzhang/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.

MOTIVATION: The risk of many complex diseases is determined by an interplay of genetic and environmental factors. The examination of gene-environment interactions (G×Es) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease-associated genes. However, the methods for testing G×Es for multiple traits are very limited. METHOD: We developed novel approaches to test G×Es for multiple traits in sequencing association studies. We first perform a transformation of multiple traits by using either principal component analysis or standardization analysis. Then, we detect the effects of G×Es using novel proposed tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ Fisher's combination test to combine the p values. RESULTS: Extensive simulation studies show that the type I error rates of the proposed methods are well controlled. Compared to the interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal G×Es. Application to the COPDGene Study demonstrates that our proposed methods are very effective. CONCLUSIONS: Our proposed methods are useful tools in the identification of G×Es for multiple traits. The proposed methods can be used not only to identify G×Es for common variants, but also for rare variants. Therefore, they can be employed in identifying G×Es in both genome-wide association studies and next-generation sequencing data analyses.

A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.

Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. In this paper, we develop a novel variable reduction method using hierarchical clustering method (HCM) for joint analysis of multiple phenotypes in association studies. The proposed method involves two steps. The first step applies a dimension reduction technique by using a representative phenotype for each cluster of phenotypes. Then, existing methods are used in the second step to test the association between genetic variants and the representative phenotypes rather than the individual phenotypes. We perform extensive simulation studies to compare the powers of multivariate analysis of variance (MANOVA), joint model of multiple phenotypes (MultiPhen), and trait-based association test that uses extended simes procedure (TATES) using HCM with those of without using HCM. Our simulation studies show that using HCM is more powerful than without using HCM in most scenarios. We also illustrate the usefulness of using HCM by analyzing a whole-genome genotyping data from a lung function study.