fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.

MOTIVATION: The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies. RESULTS: We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data. AVAILABILITY AND IMPLEMENTATION: FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

Group-based trajectory modeling for fear of cancer recurrence in cancer survivors: a systematic review.

PURPOSE: We aimed to systematically review studies that used a group-based trajectory modeling approach to explore the categories of fear of cancer recurrence (FCR) trajectories and their predictors in cancer survivors. METHODS: MEDLINE, EMBASE, CINAHL, Scopus, and Web of Science were searched. Three authors independently reviewed the literature for predefined eligibility criteria. The Joanna Briggs Institute critical appraisal tools for Cohort Studies and the Guidelines for Reporting on Latent Trajectory Studies were used to assess the quality of included studies. A qualitative synthesis of the included studies was performed. RESULTS: Ninety-eight studies were retrieved after removing duplicates, and 11 studies met the criteria for inclusion. There are four types of FCR trajectories: stable, decreasing, increasing, and stable-then-decreasing-then-increasing. The following factors were considered significant predictors of FCR trajectory category in at least one of the included studies: age, race, income, education, employment, cancer stage, physical symptoms, depression, anxiety, satisfaction with medical care, and selected cognitive and behavioral factors. CONCLUSIONS: There was considerable heterogeneity among the studies included in study design and FCR trajectory results. Factors that significantly predicted FCR trajectory categories mostly focused on psychological characteristics. The correlation of sociodemographic and disease-related predictors with FCR trajectory categories was not consistent among the included studies. IMPLICATIONS FOR CANCER SURVIVORS: We suggest that future scholars should incorporate more psychological factors when identifying cancer survivors who persistently maintain a high level of FCR and developing FCR mitigation measures.

Intestinal Ultrasound Combined with Blood Inflammatory Markers Is a More Efficient Tool in Evaluating Severity of Crohn's Disease: A Pilot Study.

Background and Aims: Intestinal ultrasound (IUS) is considered a nonirradiating, noninvasive, well-tolerated, and valuable tool for objectively assessing Crohn's disease (CD) activity. However, there is no widely accepted intestinal ultrasound scoring system. This study is aimed at evaluating the efficacy of IUS key parameters, the International Bowel Ultrasound Activity Score (IBUS-SAS), and IBUS-SAS combined with blood inflammatory markers in assessing CD activity. Methods: 40 CD patients were reviewed in this retrospective study and were divided into the moderate-severe group (n = 25) and nonmoderate-severe group (n = 15) based on a simplified endoscopic score of Crohn's disease (SES-CD). Double-balloon enteroscopy/colonoscopy were reviewed by three gastroenterologists. A transabdominal ultrasound was performed by two ultrasound specialists. Blood inflammatory markers were measured from morning samples. Results: In evaluating moderate to severe CD patients, (1) IBUS-SAS had a good predictive effect with an area-under-the-curve (AUC) of 0.855 (P < 0.001); (2) IUS key parameters (including BWT, CDS, BWS, and I-fat) yielded good predictive effects with AUC of 0.811, 0.731, 0.724, and 0.747, respectively (P < 0.001); (3) blood inflammatory markers (including ESR, CRP, PLR, MLR, and NLR) also had good predictive effects with AUC of 0.771, 0.837, 0.728, 0.743, and 0.775, respectively (P < 0.001); (4) IBUS-SAS combined with ESR and CRP exerted the best predictive effect with the highest AUC of 0.912 (95% CI: 0.823-1.000), and the sensitivity and specificity were 88.0% and 80.0%, respectively (P < 0.001). Conclusion: IBUS-SAS combined with ESR and CRP is a more efficient tool than IBUS-SAS alone or inflammatory markers alone in evaluating CD patients with moderate to severe disease activity.

gmcoda: Graphical model for multiple compositional vectors in microbiome studies.

MOTIVATION: Microbes are essential components in the ecosystem and participate in most biological procedures in environments. The high-throughput sequencing technologies help researchers directly quantify the abundance of microbes in a natural environment. Microbiome studies explore the construction, stability, and function of microbial communities with the aid of sequencing technology. However, sequencing technologies only provide relative abundances of microbes, and this kind of data is called compositional data in statistics. The constraint of the constant-sum requires flexible statistical methods for analyzing microbiome data. Current statistical analysis of compositional data mainly focuses on one compositional vector such as bacterial communities. The fungi are also an important component in microbial communities and are always measured by sequencing internal transcribed spacer instead of 16S rRNA genes for bacteria. The different sequencing methods between fungi and bacteria bring two compositional vectors in microbiome studies. RESULTS: We propose a novel statistical method, called gmcoda, based on an additive logistic normal distribution for estimating the partial correlation matrix for cross-domain interactions. A majorization-minimization algorithm is proposed to solve the optimization problem involved in gmcoda. Through simulation studies, gmcoda is demonstrated to work well in estimating partial correlations between two compositional vectors. Gmcoda is also applied to infer cross-domain interactions in a real microbiome dataset and finds potential interactions between bacteria and fungi. AVAILABILITY AND IMPLEMENTATION: Gmcoda is open source and freely available from https://github.com/huayingfang/gmcoda under GNU LGPL v3.

Gut Microbiome and Metabonomic Profile Predict Early Remission to Anti-Integrin Therapy in Patients with Moderate to Severe Ulcerative Colitis.

Patients with ulcerative colitis (UC) have low response rates to anti-integrin medications, necessitating the identification of noninvasive biomarkers for predicting remission to anti-integrin therapy. In this study, patients with moderate to severe UC commencing anti-integrin therapy (n = 29), inactive to mild UC patients (n = 13), and healthy controls (n = 11) were selected. Besides clinical evaluation, fecal samples were collected at baseline and week 14 from moderate to severe UC patients. The clinical remission was defined based on the Mayo score. Fecal samples were assessed with 16S rRNA gene sequencing, liquid chromatography-tandem mass spectrometry, and gas chromatography-mass spectrometry (GC-MS). We identified that Verrucomicrobiota was significantly more abundant in the remission group (P < 0.001) than that of nonremission group at phylum level for patients commencing vedolizumab. GC-MS analysis revealed that the concentrations of butyric acid (P = 0.024) and isobutyric acid (P = 0.042) were significantly higher in the remission group compared to the nonremission group at baseline. Finally, the combination of Verrucomicrobiota, butyric acid, and isobutyric acid improved the diagnosis of early remission to anti-integrin therapy (area under the concentration-time curve = 0.961). We identified significantly higher phylum level diversity of Verrucomicrobiota in remission than the nonremission groups at baseline. Notably, the combination of gut microbiome and metabonomic profiles improved the diagnosis of early remission to anti-integrin therapy. IMPORTANCE It is reported that patients with ulcerative colitis (UC) have low response rates to anti-integrin medications in the latest VARSITY study. Therefore, our primary goals were to discover differences in the gut microbiome and metabonomics patterns between early remission and nonremission patients and to explore the diagnostic value in predicting clinical remission to anti-integrin therapy accurately. In this study, we found that Verrucomicrobiota was significantly more abundant in the remission group (P < 0.001) than that of nonremission group at phylum level for patients commencing vedolizumab. Gas chromatography-mass spectrometry analysis revealed that the concentrations of butyric acid (P = 0.024) and isobutyric acid (P = 0.042) were significantly higher in the remission group compared with the nonremission group at baseline. Notably, the combination of Verrucomicrobiota, butyric acid, and isobutyric acid improved the diagnosis of early remission to anti-integrin therapy (area under the concentration-time curve = 0.961).

Chemo- and Diastereoselective Cycloisomerization/[2 + 3] Cycloaddition of Enynamides: Synthesis of Spiropyrazolines as Potential Anticancer Reagents.

A silver-catalyzed one-pot cycloisomerization/[2 + 3] cycloaddition of enynamides with in situ generated nitrile imines has been developed. Unlike the well-established cycloisomerization/[4 + n] cycloadditions of enynamides, this strategy provides efficient access to a new type of spiropyrazolines, which exhibit an anti-proliferation effect on multiple tumor cell lines. The compound 3u exhibits obvious anticancer activity by inducing apoptosis in RKO cells. The combination of compound 3u and an autophagy inhibitor could improve its anti-proliferation capacities.

Deep Brain Stimulation Electrode Reconstruction: Comparison between Lead-DBS and Surgical Planning System.

BACKGROUND: Electrode reconstruction for postoperative deep brain simulation (DBS) can be achieved manually using a surgical planning system such as Surgiplan, or in a semi-automated manner using software such as the Lead-DBS toolbox. However, the accuracy of Lead-DBS has not been thoroughly addressed. METHODS: In our study, we compared the DBS reconstruction results of Lead-DBS and Surgiplan. We included 26 patients (21 with Parkinson's disease and 5 with dystonia) who underwent subthalamic nucleus (STN)-DBS, and reconstructed the DBS electrodes using the Lead-DBS toolbox and Surgiplan. The electrode contact coordinates were compared between Lead-DBS and Surgiplan with postoperative CT and MRI. The relative positions of the electrode and STN were also compared between the methods. Finally, the optimal contact during follow-up was mapped onto the Lead-DBS reconstruction results to check for overlap between the contacts and the STN. RESULTS: We found significant differences in all axes between Lead-DBS and Surgiplan with postoperative CT, with the mean variance for the X, Y, and Z coordinates being -0.13, -1.16, and 0.59 mm, respectively. Y and Z coordinates showed significant differences between Lead-DBS and Surgiplan with either postoperative CT or MRI. However, no significant difference in the relative distance of the electrode and the STN was found between the methods. All optimal contacts were located in the STN, with 70% of them located within the dorsolateral region of the STN in the Lead-DBS results. CONCLUSIONS: Although significant differences in electrode coordinates existed between Lead-DBS and Surgiplan, our results suggest that the coordinate difference was around 1 mm, and Lead-DBS can capture the relative distance between the electrode and the DBS target, suggesting it is reasonably accurate for postoperative DBS reconstruction.

Comparison of dural puncture and dural incision in deep brain stimulation surgery: A simple but worthwhile technique modification.

Background: The accuracy of the deep brain stimulation (DBS) electrode placement is influenced by a myriad of factors, among which pneumocephalus and loss of cerebrospinal fluid that occurs with dural opening during the surgery are considered most important. This study aimed to describe an effective method for decreasing pneumocephalus by comparing its clinical efficacy between the two different methods of opening the dura. Materials and methods: We retrospectively compared two different methods of opening the dura in 108 patients who underwent bilateral DBS surgery in our center. The dural incision group comprised 125 hemispheres (58 bilateral and 9 unilateral) and the dural puncture group comprised 91 (41 bilateral and 9 unilateral). The volume of intracranial air, dural opening time, intraoperative microelectrode recordings (MERs), postoperative electrode displacement, clinical efficacy, and complications were examined. Spearman correlation analysis was employed to identify factors associated with the volume of intracranial air and postoperative electrode displacement. Results: The volume of intracranial air was significantly lower (0.35 cm3 vs. 5.90 cm3) and dural opening time was significantly shorter (11s vs. 35s) in the dural puncture group. The volume of intracranial air positively correlated with dural opening time. During surgery, the sensorimotor area was longer (2.47 ± 1.36 mm vs. 1.92 ± 1.42 mm) and MERs were more stable (81.82% vs. 47.73%) in the dural puncture group. Length of the sensorimotor area correlated negatively with the volume of intracranial air. As intracranial air was absorbed after surgery, significant anterior, lateral, and ventral electrode displacement occurred; the differences between the two groups were significant (total electrode displacement, 1.0mm vs. 1.4mm). Electrode displacement correlated positively with the volume of intracranial air. Clinical efficacy was better in the dural puncture group than the dural incision group (52.37% ± 16.18% vs. 43.93% ± 24.50%), although the difference was not significant. Conclusion: Our data support the hypothesis that opening the dura via puncture rather than incision when performing DBS surgery reduces pneumocephalus, shortens dural opening time, enables longer sensorimotor area and more stable MERs, minimizes postoperative electrode displacement, and may permit a better clinical efficacy.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Differential Effects of Subthalamic Nucleus and Globus Pallidus Internus Deep Brain Stimulation on Motor Subtypes in Parkinson's Disease.

OBJECTIVE: We investigated the differences in motor symptom change outcomes after bilateral subthalamic nucleus (STN) and globus pallidus internus (GPi) deep brain stimulation (DBS) in well-defined motor subtypes of Parkinson's disease (PD) to improve clinical decision making. METHODS: We included 114 patients who had undergone STN-DBS and 65 patients who had undergone GPi-DBS. The patients were classified as having akinetic-rigid type (ART), tremor-dominant type (TDT), and mixed type (MT) using the preoperative Movement Disorder Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) scores in the no-medication state. The outcome measures included the no-medication MDS-UPDRS-III scores and subscore changes at the last follow-up after surgery. The outcomes were compared among the different motor subtypes and between STN-DBS and GPi-DBS. RESULTS: At the last follow-up (14.92 ± 8.35 months), the TDT patients had had a greater median overall motor improvement in the no-medication MDS-UPDRS-III scores compared with the ART patients (62.90% vs. 46.67%; P < 0.001), regardless of the stimulation target. The ART patients showed greater improvement after STN-DBS than after GPi-DBS (54.44% vs. 37.21%; P < 0.001), with improvements in rigidity, akinesia, and posture and gait disorders accounting for the difference. CONCLUSIONS: Our results suggest that the different PD motor subtypes will have differential responses to STN-DBS and GPi-DBS, that TDT patients will experience greater improvement than ART patients, and that STN-DBS provides better effects for ART patients than does GPi-DBS. In addition, different motor symptoms among the different motor subtypes might respond differently to STN-DBS than to GPi-DBS. All these factors could reflect the heterogeneity of PD. Longer-term outcomes across the different motor subtypes and stimulation targets should be studied further.

Formal (3 + 1 + 1) Carboannulation of Morita-Baylis-Hillman Carbonates with Pyridinium Ylides: Access to Spiro-Cyclopentadiene Oxindoles.

An efficient formal (3 + 1 + 1) carboannulation strategy of Morita-Baylis-Hillman (MBH) carbonates with pyridinium ylides was developed for constructing diversely functionalized spiro-cyclopentadiene oxindoles. The reaction initiates with an SN2' olefination of MBH carbonates with pyridinium ylides. The in situ generated dienes then engage in a challenging (4 + 1) ylide carboannulation, which has been rarely reported before. The reaction features broad substrate scope as well as high chemo- and regioselectivity. (3 + 1 + 1) carboannulation products could be easily transformed into interesting spiro-cyclopenta[c]furan oxindoles.

Microstimulation Is a Promising Approach in Achieving Better Lead Placement in Subthalamic Nucleus Deep Brain Stimulation Surgery.

Background: The successful application of subthalamic nucleus (STN) deep brain stimulation (DBS) surgery relies mostly on optimal lead placement, whereas the major challenge is how to precisely localize STN. Microstimulation, which can induce differentiating inhibitory responses between STN and substantia nigra pars reticulata (SNr) near the ventral border of STN, has indicated a great potential of breaking through this barrier. Objective: This study aims to investigate the feasibility of localizing the boundary between STN and SNr (SSB) using microstimulation and promote better lead placement. Methods: We recorded neurophysiological data from 41 patients undergoing STN-DBS surgery with microstimulation in our hospital. Trajectories with typical STN signal were included. Microstimulation was applied near the bottom of STN to determine SSB, which was validated by the imaging reconstruction of DBS leads. Results: In most trajectories with microstimulation (84.4%), neuronal firing in STN could not be inhibited by microstimulation, whereas in SNr long inhibition was observed following microstimulation. The success rate of localizing SSB was significantly higher in trajectories with microstimulation than those without. Moreover, results from imaging reconstruction and intraoperative neurological assessments demonstrated better lead location and higher therapeutic effectiveness in trajectories with microstimulation and accurately identified SSB. Conclusion: Microstimulation on microelectrode recording is an effective approach to localize the SSB. Our data provide clinical evidence that microstimulation can be routinely employed to achieve better lead placement.

A Quantitative Proteome Map of the Human Body.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies.

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.

Association of APOL1 Risk Alleles With Cardiovascular Disease in Blacks in the Million Veteran Program.

BACKGROUND: Approximately 13% of black individuals carry 2 copies of the apolipoprotein L1 (APOL1) risk alleles G1 or G2, which are associated with 1.5- to 2.5-fold increased risk of chronic kidney disease. There have been conflicting reports as to whether an association exists between APOL1 risk alleles and cardiovascular disease (CVD) that is independent of the effects of APOL1 on kidney disease. We sought to test the association of APOL1 G1/G2 alleles with coronary artery disease, peripheral artery disease, and stroke among black individuals in the Million Veteran Program. METHODS: We performed a time-to-event analysis of retrospective electronic health record data using Cox proportional hazard and competing-risks Fine and Gray subdistribution hazard models. The primary exposure was APOL1 risk allele status. The primary outcome was incident coronary artery disease among individuals without chronic kidney disease during the 12.5-year follow-up period. We separately analyzed the cross-sectional association of APOL1 risk allele status with lipid traits and 115 cardiovascular diseases using phenome-wide association. RESULTS: Among 30 903 black Million Veteran Program participants, 3941 (13%) carried the 2 APOL1 risk allele high-risk genotype. Individuals with normal kidney function at baseline with 2 risk alleles had slightly higher risk of developing coronary artery disease compared with those with no risk alleles (hazard ratio, 1.11 [95% CI, 1.01-1.21]; P=0.039). Similarly, modest associations were identified with incident stroke (hazard ratio, 1.20 [95% CI, 1.05-1.36; P=0.007) and peripheral artery disease (hazard ratio, 1.15 [95% CI, 1.01-1.29l; P=0.031). When both cardiovascular and renal outcomes were modeled, APOL1 was strongly associated with incident renal disease, whereas no significant association with the CVD end points could be detected. Cardiovascular phenome-wide association analyses did not identify additional significant associations with CVD subsets. CONCLUSIONS: APOL1 risk variants display a modest association with CVD, and this association is likely mediated by the known APOL1 association with chronic kidney disease.

Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data.

MOTIVATION: Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponential-family random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g., the density, centrality, and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM. RESULTS: EM algorithm is a classical method to solve the mixture problem, however, it will be very slow when the data size is huge in the numerous applications. We adopt an efficient novel online graph clustering algorithm to classify the graph nodes and estimate the ERGM parameters for the MixtureERGM. In comparison studies, the MixtureERGM outperforms the role analysis for the network cluster in which the mixture of exponential-family random graph model is developed for many ego-network according to their roles. One genetic interaction network of yeast and two real social networks (provided as supplemental materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2017.2743711) show the wide potential application of the MixtureERGM.

Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation.

Marine Group II archaea are widely distributed in global oceans and dominate the total archaeal community within the upper euphotic zone of temperate waters. However, factors controlling the distribution of MGII are poorly delineated and the physiology and ecological functions of these still-uncultured organisms remain elusive. In this study, we investigated the planktonic MGII associated with particles and in free-living forms in the Pearl River Estuary (PRE) over a 10-month period. We detected high abundance of particle-associated MGII in PRE (up to ∼108 16S rRNA gene copies/l), which was around 10-fold higher than the free-living MGII in the same region, and an order of magnitude higher than previously reported in other marine environments. 10‰ salinity appeared to be a threshold value for these MGII because MGII abundance decreased sharply below it. Above 10‰ salinity, the abundance of MGII on the particles was positively correlated with phototrophs and MGII in the surface water was negatively correlated with irradiance. However, the abundances of those free-living MGII showed positive correlations with salinity and temperature, suggesting the different physiological characteristics between particle-attached and free-living MGIIs. A nearly completely assembled metagenome, MGIIa_P, was recovered using metagenome binning methods. Compared with the other two MGII genomes from surface ocean, MGIIa_P contained higher proportions of glycoside hydrolases, indicating the ability of MGIIa_P to hydrolyse glycosidic bonds in complex sugars in PRE. MGIIa_P is the first assembled MGII metagenome containing a catalase gene, which might be involved in scavenging reactive oxygen species generated by the abundant phototrophs in the eutrophic PRE. Our study presented the widespread and high abundance of MGII in the water columns of PRE, and characterized the determinant abiotic factors affecting their distribution. Their association with heterotrophs, preference for particles and resourceful metabolic traits indicate MGII might play a significant role in metabolising organic matters in the PRE and other temperate estuarine systems.

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations.

An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

gCoda: Conditional Dependence Network Inference for Compositional Data.

The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under this logistic normal assumption. We then propose a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data. An effective Majorization-Minimization algorithm is proposed to solve the optimization problem in gCoda. Simulation studies show that gCoda outperforms existing methods (e.g., SPIEC-EASI) in edge recovery of inverse covariance for compositional data under a variety of scenarios. gCoda also performs better than SPIEC-EASI for inferring direct microbial interactions of mouse skin microbiome data.