Parents of 5-to-12-year-old children with food allergies report more frequent use of structure-based food parenting practices.

Objectives: Food parenting practices (FPP) can have effects on children's eating behaviors. Over 8 million children in the US have food allergies, however, little is known about FPP for those who have children with food allergies. The objective of this study was to describe FPP among children with food allergies. Methods: Recruited across the United States using ResearchMatch in February and March 2021, parents of children ages 5-12 years (n = 346; n = 77 with food allergies) completed a single, online survey which measured health history, demographics, and FPP. Linear regressions were used to examine associations between FPP of children with and without food allergies, and associations between food allergy factors and FPP. Results: Parents of children with food allergies reported greater use of limit exposure than parents of children without food allergies (B = 0.131; [CI], 0.021-0.293; P = 0.024), with no differences in other types of FPP. Conclusions: Parents of children with food allergies reported more frequent structure-based FPP than parents of children without food allergies. More work is needed to explore mechanisms that promote positive food parenting among this population.

TLR7 activation of age-associated B cells mediates disease in a mouse model of primary Sjögren's disease.

Primary Sjögren's disease (pSD) (also referred to as Sjögren's syndrome) is an autoimmune disease that primarily occurs in women. In addition to exocrine gland dysfunction, pSD patients exhibit B cell hyperactivity. B cell-intrinsic TLR7 activation is integral to the pathogenesis of systemic lupus erythematosus, a disease that shares similarities with pSD. The role of TLR7-mediated B cell activation in pSD, however, remains poorly understood. We hypothesized that age-associated B cells (ABCs) were expanded in pSD and that TLR7-stimulated ABCs exhibited pathogenic features characteristic of disease. Our data revealed that ABC expansion and TLR7 expression were enhanced in a pSD mouse model in a Myd88-dependent manner. Splenocytes from pSD mice showed enhanced sensitivity to TLR7 agonism as compared with those derived from control animals. Sort-purified marginal zone B cells and ABCs from pSD mice showed enhanced inflammatory cytokine secretion and were enriched for antinuclear autoantibodies following TLR7 agonism. Finally, IgG from pSD patient sera showed elevated antinuclear autoantibodies, many of which were secreted preferentially by TLR7-stimulated murine marginal zone B cells and ABCs. These data indicate that pSD B cells are hyperresponsive to TLR7 agonism and that TLR7-activated B cells contribute to pSD through cytokine and autoantibody production. Thus, therapeutics that target TLR7 signaling cascades in B cells may have utility in pSD patients.

Sensitivity and Specificity of Exercise Intolerance on Graded Exertion Testing for Diagnosing Sport-Related Concussion: A Systematic Review and Exploratory Meta-Analysis.

Abstract There is no single gold standard test to diagnose sport-related concussion (SRC). Concussion-related exercise intolerance, that is, inability to exercise to the individual's appropriate level due to exacerbation of concussion-like symptoms, is a frequent finding in athletes early after SRC that has not been systematically evaluated as a diagnostic test of SRC. We performed a systematic review and proportional meta-analysis of studies that evaluated graded exertion testing in athletes after SRC. We also included studies of exertion testing in healthy athletic participants without SRC to assess specificity. Pubmed and Embase were searched in January 2022 for articles published since 2000. Eligible studies included those that performed graded exercise tolerance tests in symptomatic concussed participants (> 90% of subjects had an SRC, seen within 14 days of injury), at the time of clinical recovery from SRC, in healthy athletes, or both. Study quality was assessed using the Newcastle-Ottawa Scale. Twelve articles met inclusion criteria, most of which were of poor methodological quality. The pooled estimate of incidence of exercise intolerance in participants with SRC equated to an estimated sensitivity of 94.4% (95% confidence interval [CI]: 90.8, 97.2). The pooled estimate of incidence of exercise intolerance in participants without SRC equated to an estimated specificity of 94.6% (95% CI: 91.1, 97.3). The results suggest that exercise intolerance measured on systematic testing within 2 weeks of SRC may have excellent sensitivity for helping to rule in the diagnosis of SRC and excellent specificity for helping to rule out SRC. A prospective validation study to determine the sensitivity and specificity of exercise intolerance on graded exertion testing for diagnosing SRC after head injury as the source of symptoms is warranted.

MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

BACKGROUND: Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type. RESULTS: Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data. CONCLUSIONS: MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.

High precision implementation of Steck's recursion method for use in goodness-of-fit tests.

Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck's recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck's method are discussed and compared with solutions provided in high precision libraries.

Influence of Tyrosine Kinase Inhibition on Organic Anion Transporting Polypeptide 1B3-Mediated Uptake.

The organic anion transporting polypeptide family member (OATP) 1B3 is a hepatic uptake transporter that has a broad substrate recognition and plays a significant role in regulating elimination of endogenous biomolecules or xenobiotics. OATP1B3 works in tandem with OATP1B1, with which it shares approximately 80% sequence homology and a high degree of substrate overlap. Despite some substrates being recognized solely by OATP1B3, its ability to compensate for loss of OATP1B1-mediated elimination and recognition by regulatory agencies, little is known about OATP1B3 regulatory factors and how they are involved with drug-drug interaction. It was recently discovered that OATP1B1 function is mediated by the activity of a particular tyrosine kinase that is sensitive to a variety of tyrosine kinase inhibitors (TKIs). This study reports that OATP1B3 is similarly regulated, as at least 50% of its activity is reduced by 20 US Food and Drug Administration -approved TKIs. Nilotinib was assessed as the most potent OATP1B3 inhibitor among the investigated TKIs, which can occur at clinically relevant concentrations and acted predominantly through noncompetitive inhibition without impacting membrane expression. Finally, OATP1B3 function was determined to be sensitive to the knockdown of the Lck/Yes novel tyrosine kinase that is sensitive to nilotinib and has been previously implicated in mediating OATP1B1 activity. Collectively, our findings identify tyrosine kinase activity as a major regulator of OATP1B3 function which is sensitive to kinase inhibition. Given that OATP1B1 is similarly regulated, simultaneous disruption of these transporters can have drastic effects on systemic drug concentrations, which would promote adverse events. SIGNIFICANCE STATEMENT: The organic anion transporting polypeptide family member (OATP) 1B3 is a facilitator of hepatic drug elimination, although much is unknown of how OATP1B3 activity is mediated, or how such regulators contribute to drug-drug interactions. This study reports that OATP1B3 activity is dependent on the Lck/Yes novel tyrosine kinase, which is sensitive to numerous tyrosine kinase inhibitors. These findings provide insight into the occurrence of many clinical drug-drug interactions, and a rationale for future study of tyrosine kinases regulating drug disposition.

TLR7 agonism accelerates disease in a mouse model of primary Sjögren's syndrome and drives expansion of T-bet+ B cells.

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease characterized by chronic inflammation of exocrine tissue, resulting in loss of tears and saliva. Patients also experience many extra-glandular disease manifestations. Treatment for pSS is palliative, and there are currently no treatments available that target disease etiology. Previous studies in our lab demonstrated that MyD88 is crucial for pSS pathogenesis in the NOD.B10Sn-H2b (NOD.B10) pSS mouse model, although the way in which MyD88-dependent pathways become activated in disease remains unknown. Based on its importance in other autoimmune diseases, we hypothesized that TLR7 activation accelerates pSS pathogenesis. We administered the TLR7 agonist Imiquimod (Imq) or sham treatment to pre-disease NOD.B10 females for 6 weeks. Parallel experiments were performed in age and sex-matched C57BL/10 controls. Imq-treated pSS animals exhibited cervical lymphadenopathy, splenomegaly, and expansion of TLR7-expressing B cells. Robust lymphocytic infiltration of exocrine tissues, kidney and lung was observed in pSS mice following treatment with Imq. TLR7 agonism also induced salivary hypofunction in pSS mice, which is a hallmark of disease. Anti-nuclear autoantibodies, including Ro (SSA) and La (SSB) were increased in pSS mice following Imq administration. Cervical lymph nodes from Imq-treated NOD.B10 animals demonstrated an increase in the percentage of activated/memory CD4+ T cells. Finally, T-bet+ B cells were expanded in the spleens of Imq-treated pSS mice. Thus, activation of TLR7 accelerates local and systemic disease and promotes expansion of T-bet-expressing B cells in pSS.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Gene-set integrative analysis of multi-omics data using tensor-based association test.

MOTIVATION: Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. RESULTS: We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. AVAILABILITY AND IMPLEMENTATION: R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/~jytzeng/Software/TR.omics/TRinstruction.pdf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Symptoms upon postural change and orthostatic hypotension in adolescents with concussion.

Objective: Concussion is associated with dysautonomia, altered blood pressure (BP) control, and may cause Orthostatic Hypotension (OH). We measured prevalence of OH using the 1-minute supine-to-standing OH Test in adolescents with concussion and controls.Participants: Adolescents within 10 days of injury (Concussion Group, n = 297, 15.0 ± 1.7 years, 59% male) were compared with controls (Control Group, n = 214, 15.0 ± 1.5 years, 58% male).Methods: BP, heart rate (HR), and complaints of lightheadedness/dizziness were measured after 2-minute supine and 1-minute standing. Control Group was assessed once. Concussion Group was assessed twice; (1) initial visit (mean 6.0 ± 3 days-since-injury) and (2) after clinical recovery (mean 46.3 ± 42 days-since-injury).Results: Initial visit; Concussion Group reported feeling lightheaded/dizzy on postural change more often than the Control Group (37% vs 4%, p < .001) but did not differ in meeting standard OH criteria (3% vs 5%, p = .32). Experiencing symptoms did not correlate with meeting OH criteria, but correlated with abnormal vestibulo-ocular reflex. After clinical recovery; Concussion Group did not differ in experiencing lightheaded/dizziness on postural change than controls (4%, p = .65).Conclusion: Adolescents commonly experience orthostatic intolerance after concussion without meeting the standard criteria for OH.

Balanced Functional Module Detection in genomic data.

Motivation: High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks. Results: In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Identification of supervised and sparse functional genomic pathways.

Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.

The Predictive Capacity of the Buffalo Concussion Treadmill Test After Sport-Related Concussion in Adolescents.

The Buffalo Concussion Treadmill Test (BCTT) identifies the heart rate threshold (HRt) of exercise tolerance in concussed patients. A previous study found that an absolute HRt of < 135 bpm was associated with prolonged recovery (>30 days) from sport-related concussion (SRC). In this study, we assessed the relationship of ΔHR (difference between resting HR and HRt) and recovery from SRC. Using a retrospective cohort design, we compared acutely (<10 days since injury) concussed adolescents who were prescribed either (1) relative rest (RG, n = 27, 15.2 ± 1 years, 33% female, median 17 days to recovery, ΔHR = 69.6 ± 28 bpm), (2) a placebo-stretching program (PG, n = 51, 15.4 ± 2 years, 49% female, median 17 days to recovery, ΔHR = 60.9 ± 22 bpm), or (3) sub-threshold aerobic exercise (AG, n = 52, 15.3 ± 2 years, 46% female, median 13 days to recovery, ΔHR = 62.4 ± 26 bpm). Linear regression showed that ΔHR significantly correlated with duration of clinical recovery for RG (p = 0.012, R 2 = 0.228) and PG (p = 0.011, R 2 = 0.126) but not for AG (p = 0.084, R 2 = 0.059). ΔHR values were significantly lower in participants with prolonged recovery (>30 days) in RG (p = 0.01) and PG (p = 0.04). A ΔHR of ≤50 bpm on the BCTT is 73% sensitive and 78% specific for predicting prolonged recovery in concussed adolescents who were prescribed the current standard of care (i.e., cognitive and physical rest).

The salivary microbiome of diabetic and non-diabetic adults with periodontal disease.

BACKGROUND: A comparison of the salivary microbiome of non-diabetic and diabetic cohorts having periodontal health, gingivitis and periodontitis could reveal microbial signatures unique to each group that will increase understanding of the role of oral microbiota in the pathogenesis of disease, and assist with diagnosis and risk assessment for both periodontal disease and diabetes. METHODS: A group of individuals diagnosed with type 2 diabetes (T2D) was compared with a group without T2D. For both the diabetic and non-diabetic cohorts, three subgroups were established: periodontal health, gingivitis, and periodontitis. Salivary DNA was extracted (n = 146), polymerase chain reaction was performed to amplify 16S rRNA hypervariable region V3-V4, and constructed libraries were sequenced and subjected to bioinformatic and statistical analyses. RESULTS: Microbiome analysis resulted in 88 different genus level operational taxonomic units (OTUs) for differential abundance testing. Results were largely described by two trends. Trend 1 showed OTUs that increased in abundance with increasing periodontal disease, and in diabetics relative to non-diabetics. Trend 1 OTUs comprised a mix of primarily anaerobic commensals and potential periodontopathogens. Trend 2 was driven primarily by genera that decreased in abundance in those with diabetes relative to those without diabetes, which included other anaerobes associated with periodontal disease. Overall, oral microbial diversity decreased in diabetics and increased with progression of periodontal disease compared with periodontally healthy controls. CONCLUSION: Although select microbiota increased in both diabetes and periodontal disease progression, these genera decreased in co-existing diabetes and periodontal disease. These findings suggest that the genera abundance continues to change with additional stress imposed by co-existing conditions.

A Preliminary Study of Early-Onset Dementia of Former Professional Football and Hockey Players.

OBJECTIVE: To provide an overview of 3 studies of the same population of retired professional contact sport athletes compared with age-matched noncontact sport athlete controls on cognition, executive function, behavior, and advanced brain imaging. SETTING: University Concussion Management Clinic. PARTICIPANTS: Twenty-two retired professional hockey and football athletes (average age 56 years) and 21 age-matched noncontact sport athlete controls. DESIGN: Case control. MAIN MEASURES: Participants were assessed on a broad range of neuropsychological measures that are associated with identification of mild cognitive impairment and executive function. Athletes were also assessed using self-report measures of executive function and personality. Advanced structural and functional imaging techniques were utilized as well. RESULTS: The former National Football League and National Hockey League athletes perceived themselves to have impaired executive function, but this was not confirmed by objective neurocognitive assessment. No significant differences were found when comparing contact-sport athletes with controls on the presence of mild cognitive impairment or brain structural and functional tissue injury. Contact sport athletes were more anxious and more likely to report unusual beliefs and experiences. CONCLUSION: None of the retired contact sport athletes qualified as having early-onset dementia consistent with chronic traumatic encephalopathy. There were no remarkable differences in imaging, cognition, behavior, or executive function from noncontact sport athletes. The results underscore an apparent disconnect between public perceptions and evidence-based conclusions about the inevitability of chronic traumatic encephalopathy and the potential neurodegenerative effect on former athletes from contact sports.

A systematic review of criteria used to define recovery from sport-related concussion in youth athletes.

OBJECTIVE: The Concussion in Sport Group guidelines recommend a multifaceted approach to help clinicians make return to sport decisions. The purpose of this study was to identify the most common multifaceted measures used to define clinical recovery from sport-related concussion in young athletes (high school and/or college level) and to summarise existing knowledge of criteria used to make return to sport decisions. DESIGN: Systematic review. DATA SOURCES: The PubMed (MEDLINE), SPORTDiscus and Embase electronic databases were searched from 1 January 2000 to 1 March 2017 by three independent reviewers. ELIGIBILITY CRITERIA: Inclusion criteria: elementary, high school and college age groups, and a specific definition of clinical recovery that required two or more measures. EXCLUSION CRITERIA: review articles, articles using the same sample population, case studies, non-English language and those that used one measure only or did not specify the recovery measures used. STUDY QUALITY: Study quality was assessed using the Downs and Black Criteria. RESULTS: Of 2023 publications, 43 met inclusion criteria. Included articles reported the following measures of recovery: somatic symptom resolution or return to baseline (100%), cognitive recovery or return to baseline (86%), no exacerbation of symptoms on physical exertion (49%), normalisation of balance (30%), normal special physical examination (12%), successful return to school (5%), no exacerbation of symptoms with cognitive exertion (2%) and normalisation of cerebral blood flow (2%). Follow-up to validate the return to sport decision was reported in eight (19%) articles. Most studies were case-control or cohort (level of evidence 4) and had significant risk of bias. CONCLUSION: All studies of sport-related concussion use symptom reports to define recovery. A minority of studies used multiple measures of outcome or had clearly defined recovery criteria, the most common being a combination of a self-reported symptom checklist and a computerised neurocognitive test. Future studies ideally should define recovery a priori using objective physiological measures in addition to symptom reports.

Correction: Salivary inflammatory markers and microbiome in normoglycemic lean and obese children compared to obese children with type 2 diabetes.

[This corrects the article DOI: 10.1371/journal.pone.0172647.].

Salivary inflammatory markers and microbiome in normoglycemic lean and obese children compared to obese children with type 2 diabetes.

BACKGROUND: There is emerging evidence linking diabetes with periodontal disease. Diabetes is a well-recognized risk factor for periodontal disease. Conversely, pro-inflammatory molecules released by periodontally-diseased tissues may enter the circulation to induce insulin resistance. While this association has been demonstrated in adults, there is little information regarding periodontal status in obese children with and without type 2 diabetes (T2D). We hypothesized that children with T2D have higher rates of gingivitis, elevated salivary inflammatory markers, and an altered salivary microbiome compared to children without T2D. METHODS: Three pediatric cohorts ages 10-19 years were studied: lean (normal weight-C), obese (Ob), and obese with T2D (T2D). Each subject completed an oral health survey, received a clinical oral examination, and provided unstimulated saliva for measurement of inflammatory markers and microbiome analysis. RESULTS: The diabetes group was less likely to have had a dental visit within the last six months. Body mass index (BMI) Z-scores and waist circumference/height ratios were similar between Ob and T2D cohorts. The number of carious lesions and fillings were similar for all three groups. The gingival index was greater in the T2D group compared to the Ob and C groups. Although salivary microbial diversity was minimal between groups, a few differences in bacterial genus composition were noted. CONCLUSIONS: Obese children with T2D show a trend toward poorer oral health compared to normal weight and obese children without T2D. This study characterizes the salivary microbiome of children with and without obesity and T2D. This study supports a modest link between T2D and periodontal inflammation in the pediatric population.

Gene set analysis controlling for length bias in RNA-seq experiments.

BACKGROUND: In gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become an important alternative to traditional expression arrays in gene expression studies. Challenges exist in adopting the existent algorithms to RNA-seq data given the intrinsic difference of the technologies and data. In RNA-seq experiments, the measure of gene expression is correlated with gene length. This inherent correlation may cause bias in gene set analysis. RESULTS: We develop SeqGSA, a new method for gene set analysis with length bias adjustment for RNA-seq data. It extends from the R package GSA designed for microarrays. Our method compares the gene set maxmean statistic against permutations, while also taking into account of the statistics of the other gene sets. To adjust for the gene length bias, we implement a flexible weighted sampling scheme in the restandardization step of our algorithm. We show our method improves the power of identifying significant gene sets that are affected by the length bias. We also show that our method maintains the type I error comparing with another representative method for gene set enrichment test. CONCLUSIONS: SeqGSA is a promising tool for testing significant gene pathways with RNA-seq data while adjusting for inherent gene length effect. It enhances the power to detect gene sets affected by the bias and maintains type I error under various situations.