Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS.

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder.

BACKGROUND: Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients. METHODS: Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls. RESULTS: Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks. CONCLUSIONS: Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

Effects of inflammation, childhood adversity, and psychiatric symptoms on brain morphometrical phenotypes in bipolar II depression.

BACKGROUND: The neuroanatomical alteration in bipolar II depression (BDII-D) and its associations with inflammation, childhood adversity, and psychiatric symptoms are currently unclear. We hypothesize that neuroanatomical deficits will be related to higher inflammation, greater childhood adversity, and worse psychiatric symptoms in BDII-D. METHODS: Voxel- and surface-based morphometry was performed using the CAT toolbox in 150 BDII-D patients and 155 healthy controls (HCs). Partial Pearson correlations followed by multiple comparison correction was used to indicate significant relationships between neuroanatomy and inflammation, childhood adversity, and psychiatric symptoms. RESULTS: Compared with HCs, the BDII-D group demonstrated significantly smaller gray matter volumes (GMVs) in frontostriatal and fronto-cerebellar area, insula, rectus, and temporal gyrus, while significantly thinner cortices were found in frontal and temporal areas. In BDII-D, smaller GMV in the right middle frontal gyrus (MFG) was correlated with greater sexual abuse (r = -0.348, q < 0.001) while larger GMV in the right orbital MFG was correlated with greater physical neglect (r = 0.254, q = 0.03). Higher WBC count (r = -0.227, q = 0.015) and IL-6 levels (r = -0.266, q = 0.015) was associated with smaller GMVs in fronto-cerebellar area in BDII-D. Greater positive symptoms was correlated with larger GMVs of the left middle temporal pole (r = 0.245, q = 0.03). CONCLUSIONS: Neuroanatomical alterations in frontostriatal and fronto-cerebellar area, insula, rectus, temporal gyrus volumes, and frontal-temporal thickness may reflect a core pathophysiological mechanism of BDII-D, which are related to inflammation, trauma, and psychiatric symptoms in BDII-D.

Body Satisfaction, Exercise Dependence, and White Matter Microstructure in Young Adults.

BACKGROUND: Self-body satisfaction is considered a psychological factor for exercise dependence (EXD). However, the potential neuropsychological mechanisms underlying this association remain unclear. PURPOSE: To investigate the role of white matter microstructure in the association between body satisfaction and EXD. STUDY TYPE: Prospective. POPULATION: One hundred eight regular exercisers (age 22.11 ± 2.62 years; 58 female). FIELD STRENGTH/SEQUENCE: 3.0 Tesla; diffusion-weighted echo planar imaging with 30 directions. ASSESSMENT: The Body Shape Satisfaction (BSS) and Exercise Dependence Scale (EDS); whole-brain tract-based spatial statistics (TBSS) and correlational tractography analyses; average fractional anisotropy (FA) and quantitative anisotropy (QA) values of obtained tracts. STATISTICAL TESTS: The whole-brain regression model, mediation analysis, and simple slope analysis. P values <0.05 were defined as statistically significant. RESULTS: The BSS and EDS scores were 37.33 ± 6.32 and 68.22 ± 13.88, respectively. TBSS showed negative correlations between EDS and FA values in the bilateral corticospinal tract (CST, r = -0.41), right cingulum (r = -0.41), and left superior thalamic radiation (STR, r = -0.50). Correlational tractography showed negative associations between EDS and QA values of the left inferior frontal occipital fasciculus (r = -0.35), STR (r = -0.42), CST (r = -0.31), and right cingulum (r = -0.28). The FA values, rather than QA values, mediated the BSS-EDS association (indirect effects = 0.30). The BSS was significantly associated with the EDS score at both low (ß = 1.02) and high (ß = 0.43) levels of FA value, while the association was significant only at the high level of QA value (ß = 1.26). DATA CONCLUSION: EXD was correlated with white matter in frontal-subcortical and sensorimotor networks, and these tracts mediated the body satisfaction-EXD association. White matter microstructure could be a promising neural signature for understanding the underlying neuropsychological mechanisms of EXD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Analysis of Veno-Venous Extracorporeal Membrane Oxygenation for COVID-19 Compared to Non-COVID Etiologies.

OBJECTIVE: This study analyzed the characteristics and outcomes of veno-venous (V-V) extracorporeal membrane oxygenation (ECMO) for acute respiratory distress syndrome (ARDS) due to COVID-19 versus non-COVID causes at US academic centers. BACKGROUND DATA: V-V ECMO support has been utilized for COVID-19 patients with ARDS since the beginning of the pandemic. Mortality for ECMO in COVID-19 has been reported to be high but similar to reported mortality for ECMO support for non-COVID causes of respiratory failure. METHODS: Using ICD-10 codes, data of patients who underwent V-V ECMO for COVID-19 ARDS were compared with patients who underwent V-V ECMO for non-COVID causes between April 2020 and December 2022. The primary outcome was in-hospital mortality. Secondary outcome measures included length of stay and direct cost. Multivariate logistic regression modeling was performed to analyze differences in mortality between COVID and non-COVID groups, adjusting for other important risk factors (age, sex, and race/ethnicity). RESULTS: We identified and compared 6382 patients who underwent V-V ECMO for non-COVID causes to 6040 patients who underwent V-V ECMO for COVID-19. There was a significantly higher proportion of patients aged ≥ 65 years who underwent V-V ECMO in the non-COVID group compared with the COVID group (19.8% vs. 3.7%, respectively, P <0.001). Compared with patients who underwent V-V ECMO for non-COVID causes, patients who underwent V-V ECMO for COVID had increased in-hospital mortality (47.6% vs. 34.5%, P <0.001), length of stay (46.5±41.1 days vs. 40.6±46.1, P <0.001), and direct hospitalization cost ($207,022±$208,842 vs. $198,508±205,510, P =0.02). Compared with the non-COVID group, the adjusted odds ratio (OR) for in-hospital mortality in the COVID group was 2.03 (95% CI: 1.87-2.20, P <0.001). In-hospital mortality for V-V ECMO in COVID-19 improved during the study time period (50.3% in 2020, 48.6% in 2021, and 37.3% in 2022). However, there was a precipitous drop in the ECMO case volume for COVID starting in quarter 2 of 2022. CONCLUSIONS: In this nationwide analysis, COVID-19 patients with ARDS requiring V-V ECMO support had increased mortality compared with patients who underwent V-V ECMO for non-COVID etiologies.

Transcriptomics reveals a core transcriptional network of K-type cytoplasmic male sterility microspore abortion in wheat (Triticum aestivum L.).

BACKGROUND: Cytoplasmic male sterility (CMS) plays a crucial role in hybrid production. K-type CMS, a cytoplasmic male sterile line of wheat with the cytoplasms of Aegilops kotschyi, is widely used due to its excellent characteristics of agronomic performance, easy maintenance and easy restoration. However, the mechanism of its pollen abortion is not yet clear. RESULTS: In this study, wheat K-type CMS MS(KOTS)-90-110 (MS line) and it's fertile near-isogenic line MR (KOTS)-90-110 (MR line) were investigated. Cytological analysis indicated that the anthers of MS line microspore nucleus failed to divide normally into two sperm nucleus and lacked starch in mature pollen grains, and the key abortive period was the uninucleate stage to dinuclear stage. Then, we compared the transcriptome of MS line and MR line anthers at these two stages. 11,360 and 5182 differentially expressed genes (DEGs) were identified between the MS and MR lines in the early uninucleate and binucleate stages, respectively. Based on GO enrichment and KEGG pathways analysis, it was evident that significant transcriptomic differences were "plant hormone signal transduction", "MAPK signaling pathway" and "spliceosome". We identified 17 and 10 DEGs associated with the IAA and ABA signal transduction pathways, respectively. DEGs related to IAA signal transduction pathway were downregulated in the early uninucleate stage of MS line. The expression level of DEGs related to ABA pathway was significantly upregulated in MS line at the binucleate stage compared to MR line. The determination of plant hormone content and qRT-PCR further confirmed that hormone imbalance in MS lines. Meanwhile, 1 and 2 DEGs involved in ABA and Ethylene metabolism were also identified in the MAPK cascade pathway, respectively; the significant up regulation of spliceosome related genes in MS line may be another important factor leading to pollen abortion. CONCLUSIONS: We proposed a transcriptome-mediated pollen abortion network for K-type CMS in wheat. The main idea is hormone imbalance may be the primary factor, MAPK cascade pathway and alternative splicing (AS) may also play important regulatory roles in this process. These findings provided intriguing insights for the molecular mechanism of microspore abortion in K-type CMS, and also give useful clues to identify the crucial genes of CMS in wheat.

Large-scale brain functional network abnormalities in social anxiety disorder.

BACKGROUND: Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. METHODS: Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS: SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. CONCLUSIONS: SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Higher number of tacrolimus dose adjustments in kidney transplant recipients who are extensive and intermediate CYP3A5 metabolizers.

Kidney transplant recipients carrying the CYP3A5*1 allele have lower tacrolimus troughs, and higher dose requirements compared to those with the CYP3A5*3/*3 genotype. However, data on the effect of CYP3A5 alleles on post-transplant tacrolimus management are lacking. The effect of CYP3A5 metabolism phenotypes on the number of tacrolimus dose adjustments and troughs in the first 6 months post-transplant was evaluated in 78 recipients (64% Caucasians). Time to first therapeutic concentration, percentage of time in therapeutic range (TTR), and estimated glomerular filtration rate (eGFR) were also evaluated. Fifty-five kidney transplant recipients were CYP3A5 poor metabolizers (PM), 17 were intermediate metabolizers (IM), and 6 were extensive metabolizers (EM). Compared to PMs, EMs/IMs had significantly more dose adjustments (6.1 vs. 8.1, p = .015). Overall, 33.82% of trough measurements resulted in a dose change. There was no difference in the number of tacrolimus trough measurements between PMs and EM/IMs. The total daily tacrolimus dose requirements were higher in EMs and IMs compared to PMs (<.001). TTR was ∼50% in the PMs and EMs/IMs groups. CYP3A5 EM/IM metabolizers have more tacrolimus dose changes and higher dose requirements which increases clinical management complexity. Larger studies are needed to assess the cost and benefits of including genotyping data to improve clinical management.

Aberrant White Matter Microstructure in Depressed Patients with Suicidality.

BACKGROUND: Depression is a common psychiatric disorder affecting 264 million people globally, and the worst outcome is suicide. While regional brain alterations in depressed suicidal brain have previously been reported, knowledge about white matter (WM) microstructure is limited. PURPOSE: Automated fiber quantification (AFQ) acquired by magnetic resonance imaging was used to calculate diffusion properties of fiber tracks to explore the structural alteration of WM associated with suicidality in depressive patients. STUDY TYPE: Cross-sectional. SUBJECTS: Forty-five depressive patients without suicidality (DS- group, 60.00% females), 53 depressed patients with suicidality (DS+ group, 66.04% females), and 59 healthy controls (HC group, 67.80% females). FIELD STRENGTH/SEQUENCE: 3.0 T; single-shot echo-planar imaging sequence. ASSESSMENT: The point-wise group difference of the fiber tracts was determined by diffusion properties including fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of 18 specific WM tracts. STATISTICAL TESTS: Analysis of covariance (ANCOVA) and partial correlation analysis were used. A threshold of P < 0.05 was considered statistically significant. RESULTS: The significantly different diffusion properties were found in callosum forceps, left inferior fronto-occipital fasciculus (IFOF), right anterior thalamic radiation (ATR), and left cingulum cingulate in DS- and DS+ groups. The correlation analysis results showed that MD of right ATR was significantly positively correlated with Hamilton Depression Rating Scale (HAMD) scores (r = 0.363). In addition, AD of right ATR (r = 0.372), MD of callosum forceps minor (r = 0.511), RD of left IFOF (r = 0.429), and RD of callosum forceps minor (r = 0.515) were significantly positively correlated with suicide item scores of HAMD. DATA CONCLUSION: Our demonstration of decreased WM tract integrity including callosum forceps, IFOF, and ATR confirms the central involvement of the frontal cortex and limbic system with suicidality in depression. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Multivariate relationships between peripheral inflammatory marker subtypes and cognitive and brain structural measures in psychosis.

Elevations in peripheral inflammatory markers have been reported in patients with psychosis. Whether this represents an inflammatory process defined by individual or subgroups of markers is unclear. Further, relationships between peripheral inflammatory marker elevations and brain structure, cognition, and clinical features of psychosis remain unclear. We hypothesized that a pattern of plasma inflammatory markers, and an inflammatory subtype established from this pattern, would be elevated across the psychosis spectrum and associated with cognition and brain structural alterations. Clinically stable psychosis probands (Schizophrenia spectrum, n = 79; Psychotic Bipolar disorder, n = 61) and matched healthy controls (HC, n = 60) were assessed for 15 peripheral inflammatory markers, cortical thickness, subcortical volume, cognition, and symptoms. A combination of unsupervised exploratory factor analysis and hierarchical clustering was used to identify inflammation subtypes. Levels of IL6, TNFα, VEGF, and CRP were significantly higher in psychosis probands compared to HCs, and there were marker-specific differences when comparing diagnostic groups. Individual and/or inflammatory marker patterns were associated with neuroimaging, cognition, and symptom measures. A higher inflammation subgroup was defined by elevations in a group of 7 markers in 36% of Probands and 20% of HCs. Probands in the elevated inflammatory marker group performed significantly worse on cognitive measures of visuo-spatial working memory and response inhibition, displayed elevated hippocampal, amygdala, putamen and thalamus volumes, and evidence of gray matter thickening compared to the proband group with low inflammatory marker levels. These findings specify the nature of peripheral inflammatory marker alterations in psychotic disorders and establish clinical, neurocognitive and neuroanatomic associations with increased inflammatory activation in psychosis. The identification of a specific subgroup of patients with inflammatory alteration provides a potential means for targeting treatment with anti-inflammatory medications.

The Transcription Factors TaTDRL and TaMYB103 Synergistically Activate the Expression of TAA1a in Wheat, Which Positively Regulates the Development of Microspore in Arabidopsis.

Pollen fertility plays an important role in the application of heterosis in wheat (Triticum aestivum L.). However, the key genes and mechanisms underlying pollen abortion in K-type male sterility remain unclear. TAA1a is an essential gene for pollen development in wheat. Here, we explored the mechanism involved in its transcriptional regulation during pollen development, focusing on a 1315-bp promoter region. Several cis-acting elements were identified in the TAA1a promoter, including binding motifs for Arabidopsis thaliana AtAMS and AtMYB103 (CANNTG and CCAACC, respectively). Evolutionary analysis indicated that TaTDRL and TaMYB103 were the T. aestivum homologs of AtAMS and AtMYB103, respectively, and encoded nucleus-localized transcription factors containing 557 and 352 amino acids, respectively. TaTDRL and TaMYB103 were specifically expressed in wheat anthers, and their expression levels were highest in the early uninucleate stage; this expression pattern was consistent with that of TAA1a. Meanwhile, we found that TaTDRL and TaMYB03 directly interacted, as evidenced by yeast two-hybrid and bimolecular fluorescence complementation assays, while yeast one-hybrid and dual-luciferase assays revealed that both TaTDRL and TaMYB103 could bind the TAA1a promoter and synergistically increase its transcriptional activity. Furthermore, TaTDRL-EAR and TaMYB103-EAR transgenic Arabidopsis plants displayed abnormal microspore morphology, reduced pollen viability, and lowered seed setting rates. Additionally, the expression of AtMS2, a TAA1a homolog, was significantly lower in the two repressor lines than in the corresponding overexpression lines or WT plants. In summary, we identified a potential transcriptional regulatory mechanism associated with wheat pollen development.

Regional gray matter volume associated with exercise dependence: A voxel-based morphometry study.

Although regular physical exercise has multiple positive benefits for the general population, excessive exercise may lead to exercise dependence (EXD), which is harmful to one's physical and mental health. Increasing evidence suggests that stress is a potential risk factor for the onset and development of EXD. However, little is known about the neural substrates of EXD and the underlying neuropsychological mechanism by which stress affects EXD. Herein, we investigate these issues in 86 individuals who exercise regularly by estimating their cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. Whole-brain correlation analyses and prediction analyses showed negative relationships between EXD and GMV of the right orbitofrontal cortex (OFC), left subgenual cingulate gyrus (sgCG), and left inferior parietal lobe (IPL). Furthermore, mediation analyses found that the GMV of the right OFC was an important mediator between stress and EXD. Importantly, these results remained significant even when adjusting for sex, age, body mass index, family socioeconomic status, general intelligence and total intracranial volume, as well as depression and anxiety. Collectively, the results of the present study provide crucial evidence of the neuroanatomical basis of EXD and reveal a potential neuropsychological pathway in predicting EXD in which GMV mediates the relationship between stress and EXD.

Altered intrinsic brain activity in patients with hepatic encephalopathy.

Neuropsychiatric deficits are common in patients with liver cirrhosis (LC), especially in those with hepatic encephalopathy (HE). Previous studies reveal abnormalities in brain activity underlying the neuropsychiatric deficits in LC patients; however, the results are inconsistent. We conducted a meta-analysis of resting-state functional magnetic resonance imaging studies using anisotropic effect-size signed differential mapping software on LC patients to characterize the most consistent regional activity alterations, and to evaluate the potential effect of liver transplantation (LT) on brain function. Meta-regression analyses were performed to explore the relationship between brain alterations and clinical variables. Compared with healthy controls, the typical patterns of increased regional activity in the fronto-striato-cerebellar network and decreased activity in the visuo-sensorimotor network and cingulate gyrus were identified in LC patients, which remained significant in the subgroup meta-analyses of minimal HE (MHE) and overt HE (OHE) patients. Functional deficits in the default mode network (DMN) were found in OHE patients compared with MHE patients. Ammonia level positively correlated with brain activity in the right middle temporal gyrus, and the completion time of number connection test A negatively correlated with brain activity in the left anterior cingulate gyrus. In addition, patients showed increased activity in the visuo-sensorimotor network and precuneus after LT. Our study suggests that alterations in the fronto-striato-cerebellar and visuo-sensorimotor networks may be the potential pathophysiological mechanisms underlying HE, and deficits in the DMN may indicate the progression of HE. LT may improve brain function in the visuo-sensorimotor network. This study has registered in the PROSPERO (CRD42020212758).

Penalized Fieller's confidence interval for the ratio of bivariate normal means.

Constructing a confidence interval for the ratio of bivariate normal means is a classical problem in statistics. Several methods have been proposed in the literature. The Fieller method is known as an exact method, but can produce an unbounded confidence interval if the denominator of the ratio is not significantly deviated from 0; while the delta and some numeric methods are all bounded, they are only first-order correct. Motivated by a real-world problem, we propose the penalized Fieller method, which employs the same principle as the Fieller method, but adopts a penalized likelihood approach to estimate the denominator. The proposed method has a simple closed form, and can always produce a bounded confidence interval by selecting a suitable penalty parameter. Moreover, the new method is shown to be second-order correct under the bivariate normality assumption, that is, its coverage probability will converge to the nominal level faster than other bounded methods. Simulation results show that our proposed method generally outperforms the existing methods in terms of controlling the coverage probability and the confidence width and is particularly useful when the denominator does not have adequate power to reject being 0. Finally, we apply the proposed approach to the interval estimation of the median response dose in pharmacology studies to show its practical usefulness.

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.

Powerful statistical method to detect disease-associated genes using publicly available genome-wide association studies summary data.

Genome-wide association studies (GWAS) have thus far achieved substantial success. In the last decade, a large number of common variants underlying complex diseases have been identified through GWAS. In most existing GWAS, the identified common variants are obtained by single marker-based tests, that is, testing one single-nucleotide polymorphism (SNP) at a time. Generally, the basic functional unit of inheritance is a gene, rather than a SNP. Thus, results from gene-level association test can be more readily integrated with downstream functional and pathogenic investigation. In this paper, we propose a general gene-based p-value adaptive combination approach (GPA) which can integrate association evidence of multiple genetic variants using only GWAS summary statistics (either p-value or other test statistics). The proposed method could be used to test genetic association for both continuous and binary traits through not only one study but also multiple studies, which would be helpful to overcome the limitation of existing methods that can only be applied to a specific type of data. We conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We demonstrated the utility of our proposed method through analysis of GWAS meta-analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in ß -cell function, glucose homeostasis, and lipids traits.

Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis.

Patterns of change in whole-brain functional networks remain poorly understood in patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD). We conducted a prospective research to investigate the topological properties of whole-brain functional networks in those patients using a graph-based network analysis. Resting-state functional magnetic resonance imaging was performed on 51 ESRD patients (25 HD and 26 nondialysis patients) and 36 healthy controls (HCs). We compared the topological properties of brain functional networks among the three groups, and analyzed the relationships between those significant parameters and clinical variables in ESRD patients. Progressively disrupted global topological organizations were observed from nondialysis patients to HD patients compared with HCs (all p < 0.05 after Bonferroni correction). HD patients, relative to HCs, showed significantly decreased nodal centralities in the left temporal pole: superior temporal gyrus, bilateral median cingulate and paracingulate gyri, bilateral hippocampus, bilateral parahippocampal gyrus, and bilateral amygdala, and showed increased nodal centralities in the orbital part of the bilateral middle frontal gyrus, left cuneus, and left superior occipital gyrus (all p < 0.05 after Bonferroni correction). Furthermore, nodal centralities in the bilateral hippocampus were significantly decreased in HD patients compared with nondialysis patients (p < 0.05 after Bonferroni correction). Dialysis duration negatively correlated with global efficiency in ESRD patients undergoing HD (r = -0.676, FDR q = 0.004). This study indicates that ESRD patients exhibit disruptions in brain functional networks, which are more severe in HD patients, and these alterations are correlated with cognitive performance and clinical markers.

Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach.

MOTIVATION: Genetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of testing association between one trait and one genetic variant at a time in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data. RESULTS: Many existing GWAS summary data-based association test methods have relied on ad hoc approach or crude Monte Carlo approximation. In this article, we develop rigorous statistical methods for efficient and powerful multi-trait association test. We develop robust and efficient methods to accurately estimate the marginal trait correlation matrix using only GWAS summary data. We construct the principal component (PC)-based association test from the summary statistics. PC-based test has optimal power when the underlying multi-trait signal can be captured by the first PC, and otherwise it will have suboptimal performance. We develop an adaptive test by optimally weighting the PC-based test and the omnibus chi-square test to achieve robust performance under various scenarios. We develop efficient numerical algorithms to compute the analytical P-values for all the proposed tests without the need of Monte Carlo sampling. We illustrate the utility of proposed methods through application to the GWAS meta-analysis summary data for multiple lipids and glycemic traits. We identify multiple novel loci that were missed by individual trait-based association test. AVAILABILITY AND IMPLEMENTATION: All the proposed methods are implemented in an R package available at http://www.github.com/baolinwu/MTAR. The developed R programs are extremely efficient: it takes less than 2 min to compute the list of genome-wide significant single nucleotide polymorphisms (SNPs) for all proposed multi-trait tests for the lipids GWAS summary data with 2.5 million SNPs on a single Linux desktop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data.

MOTIVATION: Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data. RESULTS: We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis. AVAILABILITY AND IMPLEMENTATION: We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.