Multiple Comparisons Research Articles

Concurrent Functional Linear Regression Via Plug-in Empirical Likelihood

AbstractFunctional data with non-smooth features (e.g., discontinuities in the functional mean and/or covariance) and monotonicity arise frequently in practice. This paper develops simultaneous inference for concurrent functional linear regression in this setting. We construct a simultaneous confidence band for a functional covariate effect of interest. Along with a Wald-type formulation, our approach is based on a powerful nonparametric likelihood ratio method. Our procedures are flexible enough to allow discontinuities in the coefficient functions and the covariance structure, while accounting for discretization of the observed trajectories under a fixed dense design. A simulation study shows that the proposed likelihood ratio-based procedure outperforms the Wald-type procedure in moderate sample sizes. We apply the proposed methods to studying the effect of age on the occupation time curve derived from wearable device data obtained in an NHANES study.

Robust Open-Switch Fault Diagnosis of Bidirectional DC/DC Converters Based on Extended Kalman Filter With Multiple Corrections

Robust Open-Switch Fault Diagnosis of Bidirectional DC/DC Converters Based on Extended Kalman Filter With Multiple Corrections

JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.

It is oftentimes the case in studies of disease progression that subjects can move into one of several disease states of interest. Multistate models are an indispensable tool to analyze data from such studies. The Environmental Determinants of Diabetes in the Young (TEDDY) is an observational study of at-risk children from birth to onset of type-1 diabetes (T1D) up through the age of 15. A joint model for simultaneous inference of multistate and multivariate nonparametric longitudinal data is proposed to analyze data and answer the research questions brought up in the study. The proposed method allows us to make statistical inferences, test hypotheses, and make predictions about future state occupation in the TEDDY study. The performance of the proposed method is evaluated by simulation studies. The proposed method is applied to the motivating example to demonstrate the capabilities of the method.

Mendelian randomization study of urolithiasis: exploration of risk factors using human blood metabolites

BackgroundUrolithiasis is a highly prevalent global disease closely associated with metabolic factors; however, the causal relationship between blood metabolites and urolithiasis remains poorly understood.MethodIn our study, we employed a bi-directional two-sample Mendelian randomization (MR) analysis to investigate the causal associations between urolithiasis and metabolites. The random-effects inverse-variance weighted (IVW) estimation method was utilized as the primary approach, complemented by several other estimators including MR-Egger, weighted median, colocalization and MR-PRESSO. Furthermore, the study included replication and meta-analysis. Finally, we conducted metabolic pathway analysis to elucidate potential metabolic pathways.ResultsAfter conducting multiple tests for correction, glycerol might contribute to the urolithiasis and dehydroisoandrosterone sulfate (DHEA-S) might inhibit this process. Furthermore, several blood metabolites had shown potential associations with a causal relationship. Among the protective metabolites were lipids (dehydroisoandrosterone sulfate and 1-stearoylglycerol (1-monostearin)), amino acids (isobutyrylcarnitine and 2-aminobutyrate), a keto acid (acetoacetate) and a carbohydrate (mannose). The risk metabolites included lipids (1-palmitoylglycerophosphoethanolamine, glycerol and cortisone), a carbohydrate (erythronate), a peptide (pro-hydroxy-pro) and a fatty acid (eicosenoate). In reverse MR analysis, urolithiasis demonstrated a statistically significant causal relationship with butyrylcarnitine, 3-methyl-2-oxobutyrate, scyllo-inositol, leucylleucine and leucylalanine. However, it was worth noting that none of the blood metabolites exhibited statistical significance after multiple corrections. Additionally, we identified one metabolic pathway associated with urolithiasis.ConclusionThe results we obtained demonstrate the causal relevance between two metabolites and urolithiasis, as well as identify one metabolic pathway potentially associated with its development. Given the high prevalence of urolithiasis, further investigations are encouraged to elucidate the mechanisms of these metabolites and explore novel therapeutic strategies.

Simultaneous Inference Using Multiple Marginal Models.

This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.

Circulating metabolomics revealed novel associations between multiple ambient air pollutants exposure and chronic obstructive pulmonary disease incidence: Evidence from a prospective cohort study

The mechanisms underlying relationships between ambient air pollution and chronic obstructive pulmonary disease (COPD) risk remained largely uncertain. In this study, we aim to evaluate whether metabolic signature comprising multiple circulating metabolites can characterize metabolic response to the multiple air pollution; and to assess whether the identified metabolic signature contribute to COPD risk. A total of 227,962 participants with complete data were included from the UK biobank study. Concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter (PM2.5 and PM10) were evaluated by land-use regression models. We newly computed an air pollution score to reflect joint exposure to multiple air pollutants. Circulating metabolome was quantified by nuclear magnetic resonance (NMR) spectroscopy. During a median of 12.78 years of follow-up, a total of 8685 incident COPD cases were documented. After multiple correction, the Cox regression models showed that 102 of 143 metabolites were significantly associated with COPD risk. Utilizing elastic net regularized regressions, we identified a metabolic signature comprising 106 metabolites (including lipid, fatty acids, glycolysis and amino acids et al.) were robustly related to air pollution score. In the multivariate-adjusted Cox regression models, the derived metabolic signature showed a positive correlation with incident COPD [HR per SD = 1.20 (95% CI: 1.17–1.22)]. Casual mediation analysis further noted that the constructed metabolic signature mediated 10.5 % (8.3%–13.1%) of the air pollution-COPD associations. Taken together, our findings identified a metabolic signature that captured metabolic response to various air pollutants exposure jointly, and predicted future COPD risk independent of known risk factors.

Causal validation of the relationship between 35 blood and urine biomarkers and hyperthyroidism: a bidirectional Mendelian randomization study and meta-analysis.

Hyperthyroidism is an endocrine disorder with a relatively low global prevalence but significantly higher incidence among females compared to males. The onset age primarily ranges from 30 to 50, although it is not limited to this age group. Challenges in the treatment of hyperthyroidism include individualized treatment plan formulation, management of side effects, and prediction of disease progression, necessitating comprehensive consideration to achieve more effective therapy and management. Mendelian randomization studies can reveal more precise therapeutic targets between blood and urine biomarkers and hyperthyroidism, providing more decadent treatment options for the condition. The study will build upon the omics Mendelian randomization (MR) framework by conducting MR analysis using 35 blood and urine biomarkers separately for two distinct databases of hyperthyroidism. Subsequently, the results will undergo meta-analysis and multiple corrections to ensure accuracy and reliability. Finally, positive findings will undergo reverse MR validation to verify causal relationships with hyperthyroidism. In the British database, the MR analysis of Total bilirubin levels about hyperthyroidism yielded an odds ratio (OR) of 1.097 (95% CI: 0.951-1.265, P = 0.205). Conversely, in the Thyroid Omics Association database, the MR analysis revealed an OR of 1.283 (95% CI: 1.122-1.467, P = 0.0002) for the same relationship. Meta-analysis of the MR analysis results from both databases, following multiple corrections, resulted in an OR of 1.192 (95% CI: 1.081-1.314, P = 0.015). Additionally, the direction of beta values in the MR analysis results from both databases was consistent. The urine biomarker total bilirubin levels may contribute to an increased risk of hyperthyroidism and accelerate its progression, thus representing a risk factor for the condition.

Causal relationship analysis between 35 blood/urine metabolites and gastroesophageal reflux disease: A Mendelian randomization combined meta-analysis study.

Gastroesophageal reflux disease (GERD) is a common condition worldwide. Despite numerous studies on GERD, the causal relationships between blood/urine metabolites and GERD remain unclear. This study aims to explore the causal relationships between GERD and 35 blood/urine metabolites. In this study, we conducted Mendelian randomization (MR) analyses for 35 blood/urine metabolites with GERD phenotypes from the FinnGen R10 and UKB databases separately. We then performed a meta-analysis of the inverse variance weighted results from the 2 MR analyses and applied multiple corrections to the significant P values from the meta-analysis. Finally, we conducted reverse causality validation for the corrected positive blood/urine metabolite phenotypes with GERD. After conducting MR analysis combined with meta-analysis and performing multiple corrections, we found significant positive causal associations between only 3 blood/urine metabolites and GERD, with no significant reverse associations. Among them, 2 are risk factors for the occurrence of GERD: alanine aminotransferase levels (odds ratio (OR) = 1.120, 95% confidence interval (CI) = 1.064-1.180, P = .0005) and urate levels (OR = 1.095, 95% CI = 1.044-1.147, P = .005). Additionally, sex hormone-binding globulin levels are protective against GERD (OR = 0.928, 95% CI = 0.896-0.961, P = .0009). Elevated levels of the metabolites alanine aminotransferase and urate are associated with an increased risk of GERD, identifying them as risk factors for the condition. In contrast, higher levels of SHBG are linked to a decreased risk of GERD, indicating that SHBG is a protective factor against the disease.

Rigorous Holographic Bound on AdS Scale Separation.

We give an elementary proof of the following property of unitary, interacting four-dimensional N=2 superconformal field theories: At large central charge c, there exist at least sqrt[c] single-trace, scalar superconformal primary operators with dimensions Δ≲sqrt[c] (suppressing multiplicative logarithmic corrections). This follows from a stronger, more refined bound on the spectral density in terms of the asymptotic growth rate of the central charge. The proof employs known results on the structure of Coulomb branch operators. Interpreted holographically, this bounds the possible degree of scale separation in semiclassical AdS_{5} half-maximal supergravity. In particular, the bulk must contain an infinite tower of charged scalar states of energies parametrically below the large black hole threshold E_{BH}∼c. We address the extreme case of AdS_{5} pure supergravity, ruling it out as the asymptotic limit of certain sequences in theory space, though the general question remains open.

Transforming the Tradition of Discrete Milk Yield Correction Factors: A Continuous One-step DeLorenzo-Wiggans Method

Over the past decades, various methods have been proposed to estimate daily milk yields from partial yields. Many of these methods divide milking interval time into varied classes, assuming that the yield correction factors are constant within classes but vary between classes. The DeLorenzo and Wiggans (D-W) method has been widely used in the United States, typically following a 2-step process. It calculates discrete yield factors for segmented milking interval classes and then refines them through a follow-up smoothing step. Such a 2-step approach is computationally inefficient, and discrete yield correction factors introduce biases. This study explored strategies to integrate continuous yield factors into established methods, exemplified by the D-W method. The renovated method, also called the polynomial-interaction regression (PIR) model, postulates multiplicative yield correction factors as a linear or quadratic function of milking interval time, operating on interactions with partial yields. It utilizes all available data in a single step, exhibiting greater computability efficiency and higher estimation accuracy. A reparameterization leads to a linear model, making estimating the model parameters convenient. We evaluated the performance of the revised methods using a previous data set of milking records from Holstein cows compared with some existing methods. The results showed that the refurbished model gave more accurate estimates of daily milk yields.

Sociodemographic and Psychosocial Factors Affecting the Psychological Well-being of Preoperative Surgical Patients

PurposeThis study aimed to determine the subjective psychological well-being levels and sociodemographic and psychosocial factors affecting the psychological well-being of preoperative surgical patients. DesignThis was a cross-sectional study. MethodsThis study was conducted between January 15, 2021 and July 15, 2021. The sample consisted of 236 surgical patients in a public hospital in the Thrace region of Turkey. Data were collected using a personal information form and the five-item World Health Organization Well-Being Index (WHO-5). The data were analyzed using numbers, percentages, mean, standard deviation, independent samples t test, one-way analysis of variance, Tukey's multiple comparison test, and linear regression analysis. FindingsParticipants had a mean WHO-5 score of 10.76 ± 6.21, indicating low subjective psychological well-being. Economic status, chronic disease status, history of surgery, having been on psychiatric medication or receiving professional psychological support, tobacco use, experiencing a significant life change in the last 3 months, and reporting experiencing frequent stress in everyday life were significant predictors of WHO-5 scores. ConclusionsPreoperative surgical patients have low subjective psychological well-being, and their sociodemographic and psychosocial factors affect their psychological well-being.

Bayesian Multi-line Intensity Mapping

Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral–angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep-field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near-infrared: Hα, [O iii], Hβ, and [O ii]. We demonstrate that our method can extract the power spectrum of all four lines at the ≳10σ level at z < 2. For the brightest line, Hα, the 10σ sensitivity can be achieved out to z ∼ 3. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations.

Accurate Prediction of Children's Target Height from Their Mid-Parental Height.

Background: For the past 50 years, standard guidelines have recommended the use of sex-adjusted mid-parental height to predict a child's final height. Here, we studied the accuracy of this procedure. Methods: We used height data in a cohort of 23 very large nuclear families (mean = 11 adult children per family). We compared the actual final height of the children to their height predicted by the standard procedure, as well as to alternative height predictions that incorporate corrections of mid-parental height for age, sex, and regression to the mean. Results: Standard mid-parental height explained 36% of the variance in children's heights, with a heritability of 74%, and children were on average 2.7 cm taller than predicted by their target heights. When we introduced a nonlinear correction for the age of the parents, employed a multiplicative (rather than additive) correction for sex, and accounted for regression to the mean, the variance explained increased to 40%, heritability increased to 80%, and prediction bias was reduced from 2.7 cm to 0.14 cm (representing an improvement in prediction by half a standard deviation of the height distribution). We further measured the empirical distribution of the heights of adult children around their predicted height. We describe how this distribution can be used to estimate the probability that a child's height is within the normal expected range. Conclusions and Relevance: Based on these observations, we propose an improved method for predicting children's target heights. Our procedure for determining whether the deviation of a child's projected height from the target height is in the normal range can be used to assess whether the child should be tested further for potential medical abnormalities.

Shared aetiology underlying multiple sclerosis and other immune mediated inflammatory diseases: Swedish familial co-aggregation and large-scale genetic correlation analyses

BackgroundWhile multiple sclerosis (MS) affects less than 1 % of the general population, immune mediated inflammatory diseases (IMIDs) collectively influence 5–10 % of the population. Understanding familial co-aggregation of MS and other IMIDs carries important clinical and public health implications that will enable early detection and personalized treatment. ObjectiveTo estimate the familial association between MS and other IMIDs and to quantify their shared genetic basis. DesignRegister-based multi-generational nested case-control familial co-aggregation study and genetic correlation study. SettingSweden. Participants24,995 individuals with MS matched with 253,870 controls and 1,283,502 first-degree relatives (mothers, fathers, full siblings, and offspring) for familial co-aggregation analysis; population of European ancestry for genetic correlation analysis. MeasurementsLogistic regressions with adjustment for covariates were used to estimate the odds ratios (ORs) of developing MS in individuals with first-degree relatives diagnosed with IMIDs compared to those without such family history. Pairwise genome-wide genetic correlations were estimated with linkage-disequilibrium score regression. ResultsWe observed an OR for familial co-aggregation of MS of 1.09 (95 % confidence interval (95%CI) = 1.07−1.11) in families with IMIDs history compared to families without. The association remained broadly consistent after stratification by sex concordance of relative pairs and by kinships. 18 IMID subtypes showed a familial association with MS, 7 of which including other acute widespread myelin destruction, encephalitis or myelitis or encephalomyelitis, inflammatory bowel disease, autoimmune thyroid diseases, systemic lupus erythematosus, other inflammatory system diseases, and sarcoidosis withstood multiple correction. Genetic correlations further revealed a shared genetic basis between 7 IMID subtypes with MS. ConclusionWe demonstrated a modest familial co-aggregation of MS with several IMIDs, and such association is likely due to shared genetic factors.

Prognostic role of coronary angiography and revascularization in patients firstly admitted for heart failure in Italy

BackgroundCoronary artery disease (CAD) is a common underlying cause of de novo heart failure (HF) and is associated with poor outcome despite advances in medical therapy. There are no data clearly supporting coronary angiogram (CVG) and revascularization in this setting. MethodsWe analysed a nationwide, comprehensive, and universal administrative database of consecutive patients for the first time admitted in hospital for HF, without a history of CAD, who survived 30 days after index admission from 2015 to 2019 in Italy. Enrolled patients were classified into subjects who did not undergo CVG; those who underwent CVG without coronary revascularization; those who underwent percutaneous coronary intervention (PCI); and those who underwent coronary artery bypass grafting (CABG). ResultsDuring the study period, 342,090 patients were hospitalized for the first time due to HF and survived 30 days after admission, in Italy. Among them, 30,806 (9.0%) patients underwent CVG without undergoing coronary revascularization, 5855 (1.7%) underwent PCI and 1594 (0.5%) underwent CABG. After adjusting for age, gender and comorbidity, the hazard ratio (HR) for 1-year all-cause mortality in patients undergoing CVG vs no CVG were 0.56 (p < 0.0001), 0.66 (p < 0.0001) and 0.83 (p = 0.020) for CVG, PCI and CABG patients, respectively. When considering the re-hospitalization for HF as the outcome, using death as a competing risk, after multiple corrections, CVG (HR = 0.80; p < 0.0001) and CABG (HR = 0.73; p < 0.0002) were protective versus No CVG, but not PCI (HR = 1.02; p = 0.642). ConclusionsThis study provides evidence that CVG and coronary revascularization may be beneficial for patients with de novo HF.

Exploring the use of extended multiplicative scattering correction for near infrared spectra of wood with fungal decay

Extended Multiplicative Signal Correction (EMSC) is a multivariate linear modelling technique for multi-channel measurements that can identify and correct for different types of systematic variation patterns, known or unknown. It is typically used for pre-processing to separate light absorbance spectra, obtained by diffuse reflectance of intact samples, into three main sources of variation: additive variations due to chemical composition (≈Beer's law), mixed multiplicative and additive variations due to physical light scattering (≈Lambert's law) and more or less random measurement noise. The present work evaluates the use of EMSC to pre-process near infrared spectra obtained by hyperspectral imaging of Scots pine sapwood, inoculated with two different basidiomycete fungi and at various degradation stages. The spectral changes due to fungal decay and resulting mass loss are assessed by interpretation of the EMSC parameters and the partial least squares regression (PLSR) results. Including a cellulose (analyte) or bound water (interferent) spectral profile in the EMSC pre-processing model generally improves the predictive performance of the PLS modelling, but it can also make it worse. The inclusion of the additional polynomial baselines does not necessarily lead to a better separation of the physical and chemical effects present in the spectra. The estimated EMSC parameters provide insight into the differences in decay mechanisms. A detailed analysis of the EMSC results highlights advantages and disadvantages of using a complex pre-processing model.

Gene-age interaction study of breast cancer prognosis based on epigenomic data

Objective: Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data. Methods: Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results: The three-stage analytic strategy identified a methylation site (cg16126280EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95%CI:1.000 7-1.001 5,P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280EBF1 was completely opposite in younger patients (HR=0.550 5, 95%CI: 0.383 8-0.789 6, P=0.001) and older patients (HR=2.166 5, 95%CI: 1.285 2-3.652 2, P=0.004). Conclusions: The DNA methylation site cg16126280EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.

Distribution of Major Histocompatibility Complex Alleles in Cohorts of Patients with Different Levels of Post-­Vaccination Antibodies against Hepatitis B

Relevance. It is known that the immune response to the administration of immunobiological drugs is variable and depends on the individual characteristics of the organism. Host immunogenetic factors have a significant impact on the effectiveness of vaccination. In this study, the frequencies of alleles of the HLA class I (HLA-A, B, C) and class II genes (HLA-DRB1, DPB1, DQB1) were studied in groups of participants with different levels of antibodies (anti-HBs) after vaccination against viral hepatitis B. Aims of the work was to determine the possible relationship between alleles of HLA genes and the intensity of post-vaccination immunity against hepatitis B. Materials and methods. The study included 271 apparently healthy adults who were divided into 3 groups depending on the specific concentration of post-vaccination antibodies (anti-HBs) using ELISA. All calculations were made relative to the groups anti-HBs &gt;100 mIU/ml (n=82), 10-100 mIU/ml (n=98) (protective antibody level) and anti-HBs &lt;10 mIU/ml (n = 91). To type alleles of the HLA class I (HLA-A, B, C) and class II (HLA-DRB1, DPB1, DQB1) genes, we used a panel we developed for whole-genome next-generation sequencing (NGS). Statistical analysis was performed using Pearson's χ2 goodness-of-fit test using the FDR multiple correction method with an initial target of p &lt; 0.05. Results. When typing the six genes studied, the total number of alleles identified at least once was 189 variants that were distinct from each other. We identified 3 alleles (B*38:01:01, DQB1*06:03:01 and DRB1*13:01:01), which were significantly more common (FDR p &lt; 0.05) in the group with a protective level of anti-HBsS. Also in this group there was an increased frequency of occurrence of alleles A*26:01:01, A*32:01:01, C*12:03:01, DPB1*04:01:01 and haplotypes DQB1*06:03:01 -DRB1*13:01:01 and B*38:01:01-C*12:03:01. In the group of seronegative patients, alleles A*02:01:01, A*03:01:01, B*44:02:01, B*44:27:01, C*07:04:01, DPB1*04 were more common :01:01, DQB1*05:01:01, DRB1*01:01:01 and DRB1*16:01:01. It was shown that the identified associations were more significant in the group of individuals with a concentration of post-vaccination anti-HBs above 100 mIU/ml. Conclusion. The results obtained indicate that the HLA alleles we identified may influence the level of anti-HBsS production, and that the genetic factor may, to a greater extent, determine whether the antibody level exceeds 100 mIU/ml. defined as an anti-HBS level of 10 mIU/ ml. The development of an integrated approach to the organization of vaccine prevention, including the determination of genetic markers, will improve the quality of immunization of the population. Information about the association of HLA gene alleles can be used to develop predictive scenarios for the development of the hepatitis B epidemic process.

ANO2 Genetic Variants and Anti-VEGF Treatment Response in Neovascular AMD: A Pharmacogenetic Substudy of VIEW 1 and VIEW 2.

This analysis investigated potential associations between gene variants and clinical end points in the VIEW 1 and2 randomized clinical trials of intravitreal aflibercept and ranibizumab in neovascular age-related macular degeneration (AMD). A genome-wide association analysis was conducted in a subgroup of patients from VIEW 1 and 2 consenting to the optional pharmacogenetic analysis. Data were pooled from 780 samples from patients representative of the overall VIEW 1 and 2 populations. After Bonferroni correction for multiplicity and statistical adjustment for baseline risk factors, no significant associations were found between previously identified prognostic AMD gene variants and treatment response according to key prespecified VIEW 1 and 2 end points. Genome-wide, there were no significant genetic associations in patients experiencing gains of ≥15 Early Treatment of Diabetic Retinopathy Study letters after 1 or 2years of treatment. A cluster of variants in ANO2 (encoding anoctamin 2, a calcium-activated chloride channel expressed on photoreceptor cells) on chromosome 12 reached the level of significance for loss of ≥5 letters after 1year of treatment (P < 5 × 10-8), with the ANO2 rs2110166 SNP demonstrating highly significant association (P = 1.99 × 10-8). Carriers of the ANO2 rs2110166 TT genotype showed a robust increase in visual acuity versus baseline compared with a small decrease in those with the TC genotype. None of the potential prognostic candidate genes were associated with the clinical end points for treated patients. Preliminary analyses suggest an association of ANO2 with retinal function, with a potential impact on vision of approximately one line over at least the first year. Further investigation of the function of ANO2 in retinal pathophysiology is merited.

A - 59 MEG Power Changes in Non-concussed High School Football Players: Implications of Cumulative Head Impact Risk

Abstract Purpose The purpose of this study was to determine how cumulative repetitive head impacts (RHI) over a single season affects functional brain activity in high school football players that are clinically asymptomatic for concussion. Method Pre-season and post-season magnetoencephalography (275 channel CTF MEG) and MRI were acquired for 88 football participant seasons (mean age = 16.09 yrs, mean BMI = 26.32). Helmet-based accelerometer data was collected for each practice and game. Cumulative risk-weighted exposure from linear acceleration (RWELinear) was calculated for each subject. Standard processing and source localization of 8-minutes eyes-open-resting state MEG data was performed using Brainstorm2. Voxel-wise relative power spectral density (PSD) was calculated for the theta frequency band (5-7 Hz). After transformation to MNI space2, pre-season to post-season percent change in PSD (%theta) was calculated and spatial clusters significantly correlated with RWELinear were identified using SPM8 (a = 0.05 and false discovery correction to account for multiple corrections). Mean cluster values were extracted, and linear regression analysis performed in R 3.21 with covariates of BMI, age, and time between scans. Results After covariate adjustment, there was a significant positive correlation between RWELinear and %Δtheta PSD in the right postcentral gyrus and inferior parietal lobe (p = 9.342e-05, adj. R2 = 0.2092), with no other significant associations noted. Conclusions Changes in functional brain activity may be associated with RHI specifically in the right postcentral gyrus and inferior parietal lobe over a typical football season. Future research will include examining how these affected areas are associated with cognitive performance.