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There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and genetic variants based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by the genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose two powerful and computationally efficient gene-based association tests, Overall and Copula. These two tests aggregate information from three traditional types of gene-based association tests and also incorporate expression quantitative trait locus (eQTL) data into GWAS using GWAS summary statistics. Overall utilizes the extended Simes procedure and Copula utilizes the Gaussian copula approximation-based method. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the P values of these two methods can be calculated analytically. Simulation studies show that these two tests can control type I error rate very well and have higher power than the tests that we compared. We also apply these two methods to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that these two newly developed methods can identify more significant genes than other methods we compared with.

Wei-Tong-Xin (WTX) is a traditional Chinese medicine (TCM) that has been screened and improved in accordance with the famous ancient Chinese formula "Wan Ying Yuan". It has been shown to be clinically effective in treating gastric dysmotility, but its underlying molecular mechanism remains unclear.This study primarily dealt with the effects and mechanisms of WTX on functional dyspepsia (FD) induced by chemotherapeutic drug cisplatin (CIS).Firstly, the UPLC fingerprint and multi-component determination of WTX were established. In vivo, gastrointestinal motility of mice was detected by charcoal propulsion test. Besides, HE, western blot and qRT-PCR were performed to evaluate the occurrence of gastric antral inflammation. ROS-DHE staining was used to detect ROS levels. Further, the gut microbiota were subjected to sequencing by 16S rRNA, and the levels of bacterial metabolites short-chain fatty acids (SCFAs) and lipopolysaccharide (LPS) were detected by GC-MS and Limulus kits, respectively. The levels of GLP-1 in gastric antrum were assessed by ELISA kits. Finally, siRNA-FFAR2 experiment was performed in Raw 264.7 cells.23 common peaks were obtained from the UPLC fingerprint, and the content of 10 target components was determined. WTX increased the relative abundance of Firmicutes and decreased the number of Verrucomicrobia, accompanied by changes in the levels of SCFAs and LPS. By mediating the expression changes of free fatty acid receptor 2 (FFAR2) and toll-like receptor 4 (TLR4), WTX inhibited the phosphorylation of nuclear factor-κB (NF-κB), JNK and P38, decreased the levels of IL-1β, inducible nitric oxide synthase (iNOS) and ROS, increased the expressions of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), IL-4 and arginase-1 (Arg-1). Decreased expressions of glucagon-like peptide 1 (GLP-1) induced by WTX promoted gastric motility in FD mice. In vitro, siRNA-FFAR2 of Raw 264.7 cells eliminated the effects of WTX on TLR4 signaling pathway.In this study, the chemical profile of WTX was first reported. Based on remodeling the gut microbiota structure and adjusting the levels of metabolites (SCFAs and LPS), WTX inactivated the TLR4/MyD88 signaling pathway to inhibit the occurrence of gastric antral inflammation, which reversed the inhibitory effect of GLP-1 on gastric motility, and improved CIS-induced FD symptoms.

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure must be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

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.

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.

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis figure based on UK Biobank data for asthma shows that the proposed method is applicable to GWAS.

The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different from well-demonstrated classification applications, all output neurons in localization tasks contribute to the predicted direction, introducing much higher challenges for hardware demonstration with memristor arrays. In this work, with the proposed multi-threshold-update scheme, we experimentally demonstrate the in-situ learning ability of the sound localization function in a 1K analogue memristor array. The experimental and evaluation results reveal that the scheme improves the training accuracy by ∼45.7% compared to the existing method and reduces the energy consumption by ∼184× relative to the previous work. This work represents a significant advance towards memristor-based auditory localization system with low energy consumption and high performance.

Background: The basic helix–loop–helix (bHLH) transcription factors (TFs) serve crucial roles in the regulation of plant growth and development and usually 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 to be determined.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 bHLH proteins. A transcriptome analysis showed that some CabHLHs might be associated with the regulation of capsorubin and capsaicinoid biosynthesis, and the CabHLHs were focused mainly in cluster C1, cluster C2, cluster C3, cluster C4, cluster L5, cluster L6, cluster L8 and cluster L9. In cluster C1, cluster C2, and cluster C3, the expression profiles of CabHLH009, CabHLH032, CabHLH048, CabHLH095 and CabHLH100 were similar to the pattern of carotenoid biosynthesis in pericarp, including β-carotene, zeaxanthin, lutein and capsorubin, while the expression profiles of CabHLH007, CabHLH009, CabHLH026, CabHLH063 and CabHLH086 found in cluster L5, cluster L6 and cluster L9 were consistent with the pattern of capsaicin accumulation in the placenta. CabHLH007, CabHLH009, CabHLH026 and CabHLH086 also might be involved in temperature-mediated capsaicinoid biosynthesis. Additionally, yeast two-hybrid (Y2H) assays demonstrated that CabHLH007, CabHLH009, CabHLH026, CabHLH063 and CabHLH086 could interact with MYB31, a master regulator for capsaicinoid biosynthesis.Conclusions: The comprehensive and systematic analysis of CabHLH TFs provides significantly useful information that contributes to further investigation of CabHLHs in carotenoid and capsaicinoid biosynthesis.

Capsaicinoids confer pungency in Capsicum fruits, and the capsaicinoid content varies greatly among the five domesticated Capsicum species. Although it is generally recognized that capsaicinoid biosynthesis occurs exclusively in the placenta, few studies have focused on capsaicinoid biosynthesis gene (CBG) expression in the pericarp. Therefore, the transcriptional regulation mechanisms of capsaicinoid biosynthesis in the pericarp remain elusive. Here, the capsaicinoid contents of 32 accessions from five domesticated Capsicum species were analyzed. The results showed that the capsaicinoid contents of C. chinense accessions are significantly higher than those of the other four Capsicum species due to the increased accumulation of capsaicinoids, especially in the pericarp. Compared to that in accessions with low pericarp capsaicinoid content, the expression of the master regulator MYB31 is significantly upregulated in the pericarp in C. chinense accessions, which leads to high levels of CBG expression. Moreover, in fruits of the extremely pungent ‘Trinidad Moruga Scorpion’ (C. chinense) and low-pungency ‘59’ inbred line (C. annuum) at different developmental stages, the capsaicinoid accumulation patterns were consistent with the MYB31 and CBG expression levels in the pericarp. Taken together, our results provide novel insights into the molecular mechanism arising from the expression of a master regulator in the pericarp that results in exceedingly hot peppers. The genetic resources identified in this study could be used as genetic resources for the genetic improvement of pepper pungency.

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 (β-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.

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 (β-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.

Additional file 2: Table S1. ERF family genes in Arabidopsis, tomato and rice. Table S2. List of primers used in real-time quantitative PCR (qPCR). Table S3. CaERF genes identified and characterized in the pepper. Table S4. Biological functions of characterized ERF proteins that potentially exist in Arabidopsis and tomato. Table S5. Putative CaERF homologs (version 2.0) of pepper ERF proteins with known biological functions. Table S6. Multilevel consensus sequence identified in 144 CaERF genes.

Additional file 1: Fig. S1. Carotenoids (A) and capsaicinoids (B) biosynthetic pathways. Fig. S2. Physical distribution of all candidate CaERFs among chromosomes. Fig. S3. Multiple sequence alignment of the DREB and ERF protein subfamilies. Fig. S4. Phylogenetic tree of the pepper ERF family in relation to Arabidopsis. Fig. S5. Phylogenetic tree of the pepper ERF family in relation to tomato (137), rice (138) and Arabidopsis (122). Fig. S6. The expression patterns of CaERF genes in different tissues.

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.

Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.

Accumulating evidence suggests that lncRNAs are involved in almost all normal physiological processes and that aberrant expression of lncRNAs may be involved in the development of diseases, including non‑small cell lung cancer (NSCLC). However, the roles of lncRNA‑TPTE pseudogene 1 (TPTEP1) in lung cancer and the underlying molecular mechanisms have remained elusive. In the present study, significant downregulation of TPTEP1 in tumors compared with normal tissues from patients with NSCLC was observed. Overexpression of TPTEP1 inhibited cell proliferation and induced apoptosis in NSCLC cells. A bioinformatics analysis based on miRDB predicted microRNA (miR)‑328‑5p as a potential binding miRNA for TPTEP1. Using a dual‑luciferase reporter assay and western blot analysis, it was further validated that TPTEP1 sponged miR‑328‑5p to upregulate Src kinase signaling inhibitor 1 (SRCIN1) in NSCLC cells. Through regulation of SRCIN1, TPTEP1 was indicated to inactivate the Src and STAT3 pathways in NSCLC cells. Notably, silencing of SRCIN1 reversed the TPTEP1 overexpression‑induced inhibition of cell proliferation and increase of the apoptotic rate in NSCLC cells. Pearson correlation analysis revealed a significant positive correlation between TPTEP1 and SRCIN1 mRNA levels in NSCLC tumors. The present results provided insight into the roles of TPTEP1 in NSCLC and the underlying mechanisms.

Both genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. 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 ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose 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. We conduct extensive simulation studies to evaluate the performance of G-TOW. 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. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have 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 (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.

In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increased statistical power over analyzing each phenotype individually to identify genetic variants that are associated with complex diseases. With this motivation, several statistical methods that jointly analyze multiple phenotypes have been developed, such as O’Brien’s method, Trait-based Association Test that uses Extended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multiple phenotypes (MultiPhen). However, the performance of these methods under a wide range of scenarios is not consistent: one test may be powerful in some situations, but not in the others. Thus, one challenge in joint analysis of multiple phenotypes is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test associations between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulations are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. The simulation studies show that MultP-PE controls type I error rates very well and has consistently higher power than the tests we compared in all simulation scenarios. We conclude with the recommendation for the use of MultP-PE for its good performance in association studies with multiple phenotypes.

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, the majority of existing methods for the joint analysis of multiple traits test association between one common variant and multiple traits. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. Current statistical methods for rare variant association studies are for one single trait only. In this paper, we propose an adaptive weighting reverse regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compare the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the weighted sum reverse regression (WSRR). Our results show that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most scenarios, AWRR is more powerful than Single-TOW and WSRR.

Cholangiocarcinoma is a most lethal malignancy frequently resistant to chemotherapy. Herpes simplex virus thymidine kinase/Ganciclovir (HSV-TK/GCV) suicide gene therapy is a promising approach to treat different cancers, including cholangiocarcinoma. However drawbacks including low therapeutic gene expression and lack of precise targeted gene delivery limit the wide clinical utilization of the suicide gene therapy. We attempted to overcome these obstacles. We established the “proof-of-principle” of this concept via serial in-vitro experiments using human cholangiocarcinoma cells and then validated the new interventional oncology technique in vivo using mice harboring the same patient derived cholangiocarcinomas. Curative effects were evaluated by magnetic resonance imaging and confirmed by pathology and laboratory examinations. Intratumoral radiofrequency hyperthermia (RFH) significantly elevated the targeted expression of HSV-TK gene and further enhanced the therapeutic effects of direct intratumoral HSV-TK/GCV gene therapy, evident as the least number of survival tumor cells, smallest tumor size, and the highest apoptosis index in the combination treatment of HSV-TK plus RFH, compared to other control treatments. The novel combination of image-guided interventional oncology, RFH technology, and direct gene therapy may be valuable for the effective treatment of cholangiocarcinoma.

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.

Joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single common variant. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

Table S1. Characteristics of studies included in the meta-analysis of obesity and lung cancer risk in non-smokers. (DOCX 88 kb)

Table S2. Quality scores of the cohort studies included in the meta-analysis, assessed by the Newcastle-Ottawa scale. (DOCX 20 kb)

Many complex diseases like diabetes, hypertension, metabolic syndrome, et cetera, are measured by multiple correlated phenotypes. However, most genome-wide association studies (GWAS) focus on one phenotype of interest or study multiple phenotypes separately for identifying genetic variants associated with complex diseases. Analyzing one phenotype or the related phenotypes separately may lose power due to ignoring the information obtained by combining phenotypes, such as the correlation between phenotypes. In order to increase statistical power to detect genetic variants associated with complex diseases, we develop a novel method to test a weighted combination of multiple phenotypes (WCmulP). We perform extensive simulation studies as well as real data (COPDGene) analysis to evaluate the performance of the proposed method. Our simulation results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the methods we compared. WCmulP also has an outstanding performance for identifying single-nucleotide polymorphisms (SNPs) associated with COPD-related phenotypes.

Background Obesity is found to increase the risk of most cancer types, but reduce lung cancer risk in many studies. However, the association between obesity and lung cancer is still controversial, mainly owing to the confounding effect of smoking. Methods Eligible studies were identified from electric databases to July 1, 2017. Relevant data were extracted and pooled using random-effects models; dose-response and subgroup analyses were also performed. Results Twenty-nine studies with more than 10,000 lung cancer cases in15 million never smokers were included. Compared with normal weight, the summary relative risk (RR) was 0.77(95% confidence interval [CI]: 0.68–0.88, P

In the study of complex diseases, several correlated phenotypes are usually measured. There is also increasing evidence showing that testing the association between a single-nucleotide polymorphism (SNP) and multiple-dependent phenotypes jointly is often more powerful than analyzing only one phenotype at a time. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. In this paper, we develop an Allele-based Clustering Approach (ACA) for the joint analysis of multiple non-normal phenotypes in association studies. In ACA, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. We perform extensive simulation studies to evaluate the performance of ACA and compare the power of ACA with the powers of Adaptive Fisher’s Combination test (AFC), Trait-based Association Test that uses Extended Simes procedure (TATES), Fisher’s Combination test (FC), the standard MANOVA, and the joint model of Multiple Phenotypes (MultiPhen). Our simulation studies show that the proposed method has correct type I error rates and is much more powerful than other methods for some non-normal distributions.

Next generation sequencing technologies make direct testing rare variant associations possible. However, the development of powerful statistical methods for rare variant association studies is still underway. Most of existing methods are burden and quadratic tests. Recent studies show that the performance of each of burden and quadratic tests depends strongly upon the underlying assumption and no test demonstrates consistently acceptable power. Thus, combined tests by combining information from the burden and quadratic tests have been proposed recently. However, results from recent studies (including this study) show that there exist tests that can outperform both burden and quadratic tests. In this article, we propose three classes of tests that include tests outperforming both burden and quadratic tests. Then, we propose the optimal combination of single-variant tests (OCST) by combining information from tests of the three classes. We use extensive simulation studies to compare the performance of OCST with that of burden, quadratic and optimal single-variant tests. Our results show that OCST either is the most powerful test or has similar power with the most powerful test. We also compare the performance of OCST with that of the two existing combined tests. Our results show that OCST has better power than the two combined tests.

With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs.