Effects of a digital cognitive behavioral therapy for insomnia on sleep and alcohol consumption in heavy drinkers: A randomized pilot study.

BACKGROUND: Insomnia is a well-established, prospective risk factor for Alcohol Use Disorder. Thus, targeting sleep problems could serve as a novel and efficacious means of reducing problematic drinking. Here, we examined the potential utility of a well-validated, interactive, easy to use, self-paced digital cognitive behavioral therapy for insomnia program. In a randomized, single-blind pilot study, we examined the impact of treatment with Sleep Healthy Using the Internet (SHUTi) on drinking and sleep outcomes in a sample of heavy drinkers with insomnia. METHODS: Heavy drinking men (n = 28) and women (n = 42) with insomnia were randomly assigned to complete either the SHUTi program or a control patient education program. Subjective measures of sleep and alcohol use were administered at baseline, immediately following completion of the intervention, 3 months post-intervention, and 6 months post-intervention. Sleep outcomes were assessed using the Insomnia Severity Index and Pittsburgh Sleep Quality Index. Drinking outcomes were assessed using the 30-Day Timeline Follow-Back calendar. We used linear mixed effects models to compare groups on both insomnia and drinking outcomes. RESULTS: Data from all 70 subjects (SHUTI: n = 40; control: n = 30) were analyzed. Linear mixed effects models showed that SHUTi significantly reduced insomnia symptoms (p = 0.01) and drinking outcomes (ps < 0.05) more than the control condition over time. Trend-level effects on sleep quality (p = 0.06) were also observed. No adverse events were reported. CONCLUSIONS: Improving sleep may be an effective treatment intervention for reducing hazardous drinking in at-risk individuals. Further, findings provide preliminary support for the implementation of an easily accessible health behavior intervention with significant public health impact in a high-risk population.

Evaluation of Telehealth in Child Behavioral Health Services Delivery During the COVID-19 Pandemic.

OBJECTIVE: The authors quantified the impact of the use of telehealth services on patient-level clinical outcomes among children with complex behavioral and emotional needs in Idaho during the COVID-19 pandemic by comparing data collected in 2020 with data for the same months in 2019. METHODS: Longitudinal statewide data of Child and Adolescent Needs and Strengths (CANS) assessments were extracted from Idaho's mental and behavioral health system. Prepandemic assessments were matched to midpandemic assessments. A linear mixed-effect model was used to explore four child-level outcomes: psychosocial strengths-building rate, rate of need resolution within a life-functioning domain, rate of need resolution within a behavior-emotional domain, and rate of need resolution within a high-risk behaviors domain. RESULTS: The number of new patients admitted to Idaho's state-funded mental and behavioral health program decreased almost twofold from April-December 2019 to April-December 2020 (N=4,458 vs. 2,794). For most children with complex needs, the use of telehealth was as effective in terms of strengths building and needs resolution as in-person services; for children whose caregivers had issues with access to transportation, availability of telehealth services improved outcomes for the children. CONCLUSIONS: The COVID-19 pandemic in 2020 was associated with a dramatic drop in the number of children served by Idaho's mental health program. Telehealth may effectively bridge mental health service delivery while patients and providers work toward the resolution of transportation issues or may serve as a more acceptable permanent format of service delivery for some populations.

Measurement matters: changing penalty calculations under the hospital acquired condition reduction program (HACRP) cost hospitals millions.

BACKGROUND: Since October 2014, the Centers for Medicare and Medicaid Services has penalized 25% of U.S. hospitals with the highest rates of hospital-acquired conditions under the Hospital Acquired Conditions Reduction Program (HACRP). While early evaluations of the HACRP program reported cumulative reductions in hospital-acquired conditions, more recent studies have not found a clear association between receipt of the HACRP penalty and hospital quality of care. We posit that some of this disconnect may be driven by frequent scoring updates. The sensitivity of the HACRP penalties to updates in the program's scoring methodology has not been independently evaluated. METHODS: We used hospital discharge records from 14 states to evaluate the association between changes in HACRP scoring methodology and corresponding shifts in penalty status. To isolate the impact of changes in scoring methods over time, we used FY2018 hospital performance data to calculate total HAC scores using FY2015 through FY2018 CMS scoring methodologies. RESULTS: Comparing hospital penalty status based on various HACRP scoring methodologies over time, we found a significant overlap between penalized hospitals when using FY 2015 and 2016 scoring methodologies (95%) and between FY 2017 and 2018 methodologies (46%), but substantial differences across early vs later years. Only 15% of hospitals were eligible for penalties across all four years. We also found significant changes in a hospital's (relative) ranking across the various years, indicating that shifts in penalty status were not driven by small changes in HAC scores clustered around the penalty threshold. CONCLUSIONS: HACRP penalties have been highly sensitive to program updates, which are generally announced after performance periods are concluded. This disconnect between performance and penalties calls into question the ability of the HACRP to improve patient safety as intended.

E-Cigarette Demand: Impact of Commodity Definitions and Test-Retest Reliability.

INTRODUCTION: Behavioral economic demand provides a multidimensional understanding of reinforcement. Commodity purchase tasks are an efficient method for measuring demand in human participants. One challenge in translating these procedures to electronic nicotine delivery systems (ENDS or e-cigarettes) is defining commodity units given the lack of standardization in the e-cigarette marketplace. AIMS AND METHODS: The purpose of this study was to directly compare methods of operationalizinge-cigarette purchases, puffs, cartridges, and mLs liquid, using a within-subject design. Participants (N = 132) reporting past week e-cigarette use were recruited using crowdsourcing. Purchase tasks were completed operationalizing e-cigarette units as puffs or cartridges at baseline and puffs or mLs liquid at a 3-month follow-up. RESULTS: Bivariate associations supported convergent and discriminant validity with the largest effect size correlations for intensity and elasticity observed for the puff version. Interaction models suggested that product preferences moderated the relationship between time-to-first use and cartridge demand with larger effect size correlations among persons reporting a preference for JUULs, but weaker relationships among persons reporting other device preferences. Puff intensity (rxx = .61) and elasticity (rxx = .62) showed good test-retest reliability for participants reporting stable consumption, but poor test-retest reliability for individuals with changed consumption levels (intensity rxx = -.08; elasticity rxx = -.10). CONCLUSIONS: This study highlights the relevance of commodity definitions in the e-cigarette purchase task. Puffs as an experimental commodity may provide flexibility for studying e-cigarette demand in heterogenous or unknown populations, whereas more tailored or personalized approaches like cartridge or mL-based tasks will likely be helpful when studying known subgroups. IMPLICATIONS: The commodity purchase task procedure is widely used for understanding cigarette and e-cigarette demand in nicotine dependence research. This study evaluates the importance of operational definitions of e-cigarette commodities in the purchase task (ie, puffs, cartridges, or mLs liquid). Puffs may provide a more flexible commodity unit when evaluating e-cigarette demand in general or heterogenous populations, whereas device-specific units may prove more valuable when studying populations with consistent and known product use.

DOT: Gene-set analysis by combining decorrelated association statistics.

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.

Quantifying posterior effect size distribution of susceptibility loci by common summary statistics.

Testing millions of single nucleotide polymorphisms (SNPs) in genetic association studies has become a standard routine for disease gene discovery. In light of recent re-evaluation of statistical practice, it has been suggested that p-values are unfit as summaries of statistical evidence. Despite this criticism, p-values contain information that can be utilized to address the concerns about their flaws. We present a new method for utilizing evidence summarized by p-values for estimating odds ratio (OR) based on its approximate posterior distribution. In our method, only p-values, sample size, and standard deviation for ln(OR) are needed as summaries of data, accompanied by a suitable prior distribution for ln(OR) that can assume any shape. The parameter of interest, ln(OR), is the only parameter with a specified prior distribution, hence our model is a mix of classical and Bayesian approaches. We show that our method retains the main advantages of the Bayesian approach: it yields direct probability statements about hypotheses for OR and is resistant to biases caused by selection of top-scoring SNPs. Our method enjoys greater flexibility than similarly inspired methods in the assumed distribution for the summary statistic and in the form of the prior for the parameter of interest. We illustrate our method by presenting interval estimates of effect size for reported genetic associations with lung cancer. Although we focus on OR, the method is not limited to this particular measure of effect size and can be used broadly for assessing reliability of findings in studies testing multiple predictors.

Inflammatory properties of diet mediate the effect of depressive symptoms on Framingham risk score in men and women: Results from the National Health and Nutrition Examination Survey (2007-2014).

Depression is common in patients with cardiovascular disease (CVD) and associated with inflammation. Inflammation contributes to the development of CVD and can be modulated by diet. However, the role of inflammatory properties of diet in the relationship between depressive symptoms and CVD risk is not well understood. We hypothesized that the inflammatory properties of diet mediate the relationship between depressive symptoms and CVD risk in men and women. Cross-sectional data collected by the National Health and Nutrition Examination Survey (2007-2014) were used for the study. Depressive symptoms scores, inflammatory properties of diet, and CVD risk were measured by the Patient Health Questionnaire-9 (PHQ-9), the Dietary Inflammatory Index (DII), and the Framingham risk score (FRS), respectively. Generalized linear models were used for the mediation analysis. There were significant differences in the proportions of men and women in the depressed group (PHQ-9 ≥ 10; 5.24 ±â€¯0.65% vs 9.36 ±â€¯0.87%, P < .001) and high CVD risk group (FRS >20%; 16.47 ±â€¯0.79% vs 6.03 ±â€¯0.32%, P < .001). The DII partially mediated the relationship between depressive symptoms and CVD risk in men (indirect effect: 0.06, P = .010) but fully mediated the relationship between depressive symptoms and CVD risk in women (indirect effect: 0.10, P < .001). These findings confirmed our hypothesis that inflammatory properties of diet at least partially mediate the relationship between depressive symptoms and CVD risk in men and women. Our findings suggest that interventions designed to reduce depressive symptoms should contain strategies to reduce pro-inflammatory and increase anti-inflammatory properties of diet to decrease CVD risk.

Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies.

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the rank truncated product (RTP), have been developed for combining top-ranking associations, and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking single nucleotide polymorphisms (SNPs), while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation and practical implementation and hinders further developments. Here, we propose the augmented rank truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of µ-opioid receptor variants with sensitivity to pain.

Earlier tracheostomy and percutaneous endoscopic gastrostomy in patients with hemorrhagic stroke: associated factors and effects on hospitalization.

OBJECTIVE: Existing literature supports benefits of early tracheostomy and percutaneous endoscopic gastrostomy (PEG) in certain patient populations. The aim of this study was to review tracheostomy and PEG placement data in patients with hemorrhagic stroke in order to identify factors associated with earlier placement and to evaluate outcomes. METHODS: The authors performed a retrospective review of consecutive patients treated for hemorrhagic stroke between June 1, 2011, and June 1, 2015. Data were analyzed by logistic and multiple linear regression. RESULTS: Of 240 patients diagnosed with hemorrhagic stroke, 31.25% underwent tracheostomy and 35.83% underwent PEG tube placement. Factors significantly associated with tracheostomy and PEG included the presence of pneumonia on admission and subarachnoid hemorrhage. Earlier tracheostomy was significantly associated with shorter ICU length of stay; earlier tracheostomy and PEG placement were associated with shorter overall hospitalization. Timing of tracheostomy and PEG was not significantly associated with patient survival or the incidence of complications in this population. CONCLUSIONS: This study identified patient risk factors associated with increased likelihood of tracheostomy and PEG in patients with hemorrhagic stroke who were critically ill. Additionally, we found that the timing of tracheostomy was associated with length of ICU stay and overall hospital stay, and that the timing of PEG was associated with overall length of hospitalization. Complication rates related to tracheostomy and PEG in this population were minimal. This retrospective data set supports some benefit to earlier tracheostomy and PEG placement in this population and justifies the need for further prospective study.

Ticagrelor Reduces Thromboinflammatory Markers in Patients With Pneumonia.

Despite treatment advances for sepsis and pneumonia, significant improvements in outcome have not been realized. Antiplatelet therapy may improve outcome in pneumonia and sepsis. In this study, the authors show that ticagrelor reduced leukocytes with attached platelets as well as the inflammatory biomarker interleukin (IL)-6. Pneumonia patients receiving ticagrelor required less supplemental oxygen and lung function tests trended toward improvement. Disruption of the P2Y12 receptor in a murine model protected against inflammatory response, lung permeability, and mortality. Results indicate a mechanistic link between platelets, leukocytes, and lung injury in settings of pneumonia and sepsis, and suggest possible therapeutic approaches to reduce complications.(Targeting Platelet-Leukocyte Aggregates in Pneumonia With Ticagrelor [XANTHIPPE]; NCT01883869).

The more you test, the more you find: The smallest P-values become increasingly enriched with real findings as more tests are conducted.

The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to factors such as adoption of better statistical practices and availability of larger sample sizes. Here, we suggest that another important factor behind the improved replicability of genome-wide scans is an increase in the amount of statistical testing itself. We show that an increase in the number of tested hypotheses increases the proportion of true associations among the variants with the smallest P-values. We develop statistical theory to quantify how the expected proportion of genuine signals (EPGS) among top hits depends on the number of tests. This enrichment of top hits by real findings holds regardless of whether genome-wide statistical significance has been reached in a study. Moreover, if we consider only those "failed" studies that produce no statistically significant results, the same enrichment phenomenon takes place: the proportion of true associations among top hits grows with the number of tests. The enrichment occurs even if the true signals are encountered at the logarithmically decreasing rate with the additional testing.

Estimated probability of becoming a case of drug dependence in relation to duration of drug-taking experience: a functional analysis approach.

Measured as elapsed time from first use to dependence syndrome onset, the estimated "induction interval" for cocaine is thought to be short relative to the cannabis interval, but little is known about risk of becoming dependent during first months after onset of use. Virtually all published estimates for this facet of drug dependence epidemiology are from life histories elicited years after first use. To improve estimation, we turn to new month-wise data from nationally representative samples of newly incident drug users identified via probability sampling and confidential computer-assisted self-interviews for the United States National Surveys on Drug Use and Health, 2004-2013. Standardized modules assessed first and most recent use, and dependence syndromes, for each drug subtype. A four-parameter Hill function depicts the drug dependence transition for subgroups defined by units of elapsed time from first to most recent use, with an expectation of greater cocaine dependence transitions for cocaine versus cannabis. This study's novel estimates for cocaine users one month after first use show 2-4% with cocaine dependence; 12-17% are dependent when use has persisted. Corresponding cannabis estimates are 0-1% after one month, but 10-23% when use persists. Duration or persistence of cannabis smoking beyond an initial interval of a few months of use seems to be a signal of noteworthy risk for, or co-occurrence of, rapid-onset cannabis dependence, not too distant from cocaine estimates, when we sort newly incident users into subgroups defined by elapsed time from first to most recent use. Copyright © 2016 John Wiley & Sons, Ltd.

The impact of genetic structure on sequencing analysis.

BACKGROUND: Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutionary theory and empirical studies suggest that rare mutations could play an important role in human diseases, which motivates comprehensive investigation of rare variants in sequencing studies. To explore the association of rare variants with human diseases, many statistical approaches have been developed with different ways of modeling genetic structure (ie, linkage disequilibrium). Nevertheless, the appropriate strategy to model genetic structure of sequencing data and its effect on association analysis have not been well studied. METHODS: We investigate 3 statistical approaches that use 3 different strategies to model the genetic structure of sequencing data. We proceed by comparing a burden test that assumes independence among sequencing variants, a burden test that considers pairwise linkage disequilibrium (LD), and a functional analysis of variance (FANOVA) test that models genetic data through fitting continuous curves on individuals' genotypes. RESULTS: Through simulations, we find that FANOVA attains better or comparable performance to the 2 burden tests. Overall, the burden test that considers pairwise LD has comparable performance to the burden test that assumes independence between sequencing variants. However, for 1 gene, where the disease-associated variant is located in an LD block, we find that considering pairwise LD could improve the test's performance. CONCLUSIONS: The structure of sequencing variants is complex in nature and its patterns vary across the whole genome. In certain cases (eg, a disease-susceptibility variant is in an LD block), ignoring the genetic structure in the association analysis could result in suboptimal performance. Through this study, we show that a functional-based method is promising for modeling the underlying genetic structure of sequencing data, which could lead to better performance.

Inter-relationships Linking Probability of Becoming a Case of Nicotine Dependence With Frequency of Tobacco Cigarette Smoking.

INTRODUCTION: Once smoking starts, some tobacco cigarette smokers (TCS) can make very rapid transitions into tobacco dependence syndromes (TCD). With adjustment for smoking frequency, we posit female excess risk for this rapid-onset TCD. In a novel application of functional analysis for tobacco research, we estimate four Hill function parameters and plot TCD risk against a gradient of smoking frequency, as observed quite soon after smoking onset. METHODS: In aggregate, the National Surveys of Drug Use and Health, 2004-2013, identified 1546 newly incident TCS in cross-sectional research, each with standardized TCD assessment. RESULTS: Hill function estimates contradict our apparently over-simplistic hypothesis. Among newly incident TCS males with only 1-3 recent smoking days, an estimated 1%-3% had become rapid-onset TCD cases; non-overlapping confidence intervals show lower TCD risk for females. In contrast, among daily smokers, closer to 50% of female TCS showed rapid-onset TCD, versus under 20% of male TCS, but a larger sample will be needed to confirm the apparent female excess risk at the daily smoking frequency level. CONCLUSIONS: Smoking frequency and TCD onset become inter-dependent quite soon after TCS onset. Feedback loops are expected, and might explain a potential reversal of male-female differences across smoking frequency gradients. These novel epidemiological estimates prompt new thinking and questions about interventions. IMPLICATIONS: In this large sample epidemiological study, with a nationally representative sample of newly incident TCS assessed cross-sectionally, we see a quite rapid onset of tobacco dependence, with an early male excess that fades out at higher levels of smoking frequency. Next steps include development of outreach and intervention for this very rapid-onset tobacco dependence.

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.

Transitioning from First Drug Use to Dependence Onset: Illustration of a Multiparametric Approach for Comparative Epidemiology.

Studying transitions from first drug use (DU) to drug dependence (DD) onset, we estimate a parsimonious set of parameters based on epidemiological data, with plans for future longitudinal research on newly incident drug users and with tracking of self-administration frequencies and DD clinical features. Our expectation is a distinctive sigmoid pattern with one asymptote for lower DD probability (when DU is insubstantial), upturning slopes of DD risk beyond a middle value (PD50), and eventual higher DD risk asymptotes at higher DU frequencies. We illustrate this novel approach using cross-sectional data from the United States National Surveys on Drug Use and Health, 2002-2011. Empirical DD probabilities observed soon after newly incident use are estimated across DU frequency values, using parametric Hill functions and four governing parameters for differential comparison across drugs and DU subgroups. Among drug subtypes considered, cocaine shows larger estimates, especially among females (estimated P(min)=7% for females vs 3% for males; p<0.001), for whom PD50 is shorter by 8 days of use (p=0.027), conditional on the same rate of use in the past 30 days. Clear alcohol male-female differences also are observed (eg, female PD50 < male PD50; p=0.002). Although based on cross-sectional snapshots soon after DU onset, this novel multiparametric statistical approach for comparative epidemiological DD research creates new opportunities in planned studies with prospectively gathered longitudinal data. The cross-sectional estimates provide starting values needed to plan future longitudinal research programs on transitions from initial DU until formation of a DD syndrome.

Assessing the Probability that a Finding Is Genuine for Large-Scale Genetic Association Studies.

Genetic association studies routinely involve massive numbers of statistical tests accompanied by P-values. Whole genome sequencing technologies increased the potential number of tested variants to tens of millions. The more tests are performed, the smaller P-value is required to be deemed significant. However, a small P-value is not equivalent to small chances of a spurious finding and significance thresholds may fail to serve as efficient filters against false results. While the Bayesian approach can provide a direct assessment of the probability that a finding is spurious, its adoption in association studies has been slow, due in part to the ubiquity of P-values and the automated way they are, as a rule, produced by software packages. Attempts to design simple ways to convert an association P-value into the probability that a finding is spurious have been met with difficulties. The False Positive Report Probability (FPRP) method has gained increasing popularity. However, FPRP is not designed to estimate the probability for a particular finding, because it is defined for an entire region of hypothetical findings with P-values at least as small as the one observed for that finding. Here we propose a method that lets researchers extract probability that a finding is spurious directly from a P-value. Considering the counterpart of that probability, we term this method POFIG: the Probability that a Finding is Genuine. Our approach shares FPRP's simplicity, but gives a valid probability that a finding is spurious given a P-value. In addition to straightforward interpretation, POFIG has desirable statistical properties. The POFIG average across a set of tentative associations provides an estimated proportion of false discoveries in that set. POFIGs are easily combined across studies and are immune to multiple testing and selection bias. We illustrate an application of POFIG method via analysis of GWAS associations with Crohn's disease.

Resampling-based multiple comparison procedure with application to point-wise testing with functional data.

In this paper we describe a coherent multiple testing procedure for correlated test statistics such as are encountered in functional linear models. The procedure makes use of two different p-value combination methods: the Fisher combination method and the Sidák correction-based method. P-values for Fisher's and Sidák's test statistics are estimated through resampling to cope with the correlated tests. Building upon these two existing combination methods, we propose the smallest p-value as a new test statistic for each hypothesis. The closure principle is incorporated along with the new test statistic to obtain the overall p-value and appropriately adjust the individual p-values. Furthermore, a shortcut version for the proposed procedure is detailed, so that individual adjustments can be obtained even for a large number of tests. The motivation for developing the procedure comes from a problem of point-wise inference with smooth functional data where tests at neighboring points are related. A simulation study verifies that the methodology performs well in this setting. We illustrate the proposed method with data from a study on the aerial detection of the spectral effect of below ground carbon dioxide leakage on vegetation stress via spectral responses.

Functional analysis of variance for association studies.

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.