Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Psychometric Evaluation of the Patient-Reported Outcomes Measurement Information System Fatigue-Short Form Across Diverse Populations.

BACKGROUND: The need for reliable, valid tools to measure patient-reported outcomes (PROs) is critical both for research and for evaluating treatment effects in practice. The Patient-Reported Outcomes Measurement Information System Fatigue-Short Form v1.0-Fatigue 7a (PROMIS F-SF) has had limited psychometric evaluation in various populations. OBJECTIVES: The aim of the study is to examine psychometric properties of PROMIS F-SF item responses across various populations. METHODS: Data from five studies with common data elements were used in this secondary analysis. Samples from patients with fibromyalgia, sickle cell disease, cardiometabolic risk, pregnancy, and healthy controls were used. Reliability was estimated using Cronbach's alpha. Dimensionality was evaluated with confirmatory factor analysis. Concurrent validity was evaluated by examining Pearson's correlations between scores from the PROMIS F-SF, the Multidimensional Fatigue Symptom Inventory-Short Form, and the Brief Fatigue Inventory. Discriminant validity was evaluated by examining Pearson's correlations between scores on the PROMIS F-SF and measures of stress and depressive symptoms. Known groups validity was assessed by comparing PROMIS F-SF scores in the clinical samples to healthy controls. RESULTS: Reliability of PROMIS F-SF scores was adequate across samples, ranging from .72 in the pregnancy sample to .88 in healthy controls. Unidimensionality was supported in each sample. Concurrent validity was strong; across the groups, correlations with scores on the Multidimensional Fatigue Symptom Inventory-Short Form and Brief Fatigue Inventory ranged from .60 to .85. Correlations of the PROMIS F-SF with measures of stress and depressive mood were moderate to strong, ranging from .37 to .64. PROMIS F-SF scores were significantly higher in clinical samples compared to healthy controls. DISCUSSION: Reliability and validity of the PROMIS F-SF were acceptable. The PROMIS F-SF is a suitable measure of fatigue across the four diverse clinical populations included in the analysis.

Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery.

Introduction: The availability of large-scale biobanks linking genetic data, rich phenotypes, and biological measures is a powerful opportunity for scientific discovery. However, real-world collections frequently have extensive missingness. While missing data prediction is possible, performance is significantly impaired by block-wise missingness inherent to many biobanks. Methods: To address this, we developed Missingness Adapted Group-wise Informed Clustered (MAGIC)-LASSO which performs hierarchical clustering of variables based on missingness followed by sequential Group LASSO within clusters. Variables are pre-filtered for missingness and balance between training and target sets with final models built using stepwise inclusion of features ranked by completeness. This research has been conducted using the UK Biobank (n > 500 k) to predict unmeasured Alcohol Use Disorders Identification Test (AUDIT) scores. Results: The phenotypic correlation between measured and predicted total score was 0.67 while genetic correlations between independent subjects was high >0.86. Discussion: Phenotypic and genetic correlations in real data application, as well as simulations, demonstrate the method has significant accuracy and utility for increasing power for genetic loci discovery.

Multifaceted risk for non-suicidal self-injury only versus suicide attempt in a population-based cohort of adults.

BACKGROUND: Non-suicidal self-injury and suicide attempt represent significant public health concerns. While these outcomes are related, there is prior evidence that their etiology does not entirely overlap. Efforts to directly differentiate risk across outcomes are uncommon, particularly among older, population-based cohorts. METHODS: This research has been conducted using the UK Biobank. Data on individuals' self-reported history of non-suicidal self-injury only versus suicide attempt (maximum N = 6643) were analyzed. Applying LASSO and standard logistic regression, participants reporting one of these outcomes were assessed for differences across a range of sociodemographic, behavioral, and environmental features. RESULTS: Sociodemographic features most strongly differentiated between the outcomes of non-suicidal self-injury only versus suicide attempt. Specifically, Black individuals were more likely to report a suicide attempt, as were those of mixed race, those endorsing higher levels of depressive symptoms or trauma history, and those who had experienced financial problems (odds ratios 1.02-3.92). Those more likely to engage in non-suicidal self-injury only were younger, female, had higher levels of education, those who resided with a partner, and those who had a recently injured relative. LIMITATIONS: Differences in timing across correlates and outcomes preclude the ability to establish causal pathways. CONCLUSIONS: The factors identified in the current study as differentially associated with non-suicidal self-injury only versus suicide attempt provide further evidence of at least partially distinct correlates, and warrant follow-up in independent samples to investigate causality.

Preliminary assessment of three quantitative approaches for estimating time-since-deposition from autofluorescence and morphological profiles of cell populations from forensic biological samples.

Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a sample's 'time-since-deposition' (TSD), can provide critical context for biological evidence. Yet, there remains no analytical techniques for TSD that are validated for forensic casework. In this study, we investigate whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of 'touch' or trace biological samples. To this end, three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, we transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94-96% and 98-99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework.

Differentiation of vaginal cells from epidermal cells using morphological and autofluorescence properties: Implications for sexual assault casework involving digital penetration.

Analysis of DNA mixtures from sexual assault evidence is an ongoing challenge for DNA casework laboratories. To assist the forensic scientist address source and activity level propositions there is a significant need for new techniques that can provide information as to the source of DNA, particularly for sexual assault samples that do not involve semen. The goal of this study was to develop a new biological signature system that provides additional probative value to samples comprised of mixtures of epidermal and vaginal cells, as may be observed in cases involving digital penetration. Signatures were based on morphological and autofluorescence properties of individual cells collected through Imaging Flow Cytometry (IFC). Comparisons to reference cell populations from vaginal tissue and epidermal cells collected from hands showed strong multivariate differences across > 80 cellular measurements. These differences were used to build a predictive framework for classifying unknown cell populations as originating from epithelial cells associated with digital penetration or epidermal tissue. As part of the classification scheme, posterior probabilities of specific tissue group membership were calculated for each cell, along with multivariate similarity to that tissue type. We tested this approach on cell populations from reference tissue as well as mock casework samples involving hand swabbings following digital vaginal penetration. Many more cells classifying as non-epidermal tissue were detected in digital penetration hand swab samples than control hand swabbings. Minimum interpretation thresholds were developed to minimize false positives; these thresholds were also effective when screening licked hands, indicating the potential utility of this method for a variety of biological mixture types and depositional events relevant to forensic casework. Results showed that samples collected subsequent to digital penetration possessed markedly higher numbers of cells classifying as vaginal tissue as well as higher posterior probabilities for vaginal tissue (≥ 0.90) compared to cell populations collected from hands without prior contact with vaginal tissue. Additionally, digital penetration cell populations may be resolved from saliva cell populations and other non-target tissue types.

Case-only exome variation analysis of severe alcohol dependence using a multivariate hierarchical gene clustering approach.

BACKGROUND: Variation in genes involved in ethanol metabolism has been shown to influence risk for alcohol dependence (AD) including protective loss of function alleles in ethanol metabolizing genes. We therefore hypothesized that people with severe AD would exhibit different patterns of rare functional variation in genes with strong prior evidence for influencing ethanol metabolism and response when compared to genes not meeting these criteria. OBJECTIVE: Leverage a novel case only design and Whole Exome Sequencing (WES) of severe AD cases from the island of Ireland to quantify differences in functional variation between genes associated with ethanol metabolism and/or response and their matched control genes. METHODS: First, three sets of ethanol related genes were identified including those a) involved in alcohol metabolism in humans b) showing altered expression in mouse brain after alcohol exposure, and altering ethanol behavioral responses in invertebrate models. These genes of interest (GOI) sets were matched to control gene sets using multivariate hierarchical clustering of gene-level summary features from gnomAD. Using WES data from 190 individuals with severe AD, GOI were compared to matched control genes using logistic regression to detect aggregate differences in abundance of loss of function, missense, and synonymous variants, respectively. RESULTS: Three non-independent sets of 10, 117, and 359 genes were queried against control gene sets of 139, 1522, and 3360 matched genes, respectively. Significant differences were not detected in the number of functional variants in the primary set of ethanol-metabolizing genes. In both the mouse expression and invertebrate sets, we observed an increased number of synonymous variants in GOI over matched control genes. Post-hoc simulations showed the estimated effects sizes observed are unlikely to be under-estimated. CONCLUSION: The proposed method demonstrates a computationally viable and statistically appropriate approach for genetic analysis of case-only data for hypothesized gene sets supported by empirical evidence.

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information.

Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default = 1.06 x 10 -9 and P Empirical weight = 6.25 x 10 -11 ). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.

Differential DNA Methylation and Cardiometabolic Risk in African American Mother-Adolescent Dyads.

BACKGROUND: Cardiovascular disease disproportionately affects African Americans as the leading cause of morbidity and mortality. Among African Americans, compared to other racial groups, cardiovascular disease onset occurs at an earlier age due to a higher prevalence of cardiometabolic risk factors, particularly obesity, hypertension and type 2 diabetes. Emerging evidence suggests that heritable epigenetic processes are related to increased cardiovascular disease risk, but this is largely unexplored in adolescents or across generations. MATERIALS AND METHODS: In a cross-sectional descriptive pilot study in low-income African American mother-adolescent dyads, we examined associations between DNA methylation and the cardiometabolic indicators of body mass index, waist circumference, and insulin resistance. RESULTS: Four adjacent cytosine and guanine nucleotides (CpG) sites were significantly differentially methylated and associated with C-reactive protein (CRP), 62 with waist circumference, and none to insulin resistance in models for both mothers and adolescents. CONCLUSION: Further study of the relations among psychological and environmental stressors, indicators of cardiovascular disease, risk, and epigenetic factors will improve understanding of cardiovascular disease risk so that preventive measures can be instituted earlier and more effectively. To our knowledge this work is the first to examine DNA methylation and cardiometabolic risk outcomes in mother-adolescent dyads.

COVID and college: how the pandemic impacted alcohol use disorder status among students.

Objective: Alcohol consumption patterns during the COVID-19 pandemic have varied notably. Participants: We examined the acute impact of the pandemic on alcohol use disorder (AUD) in a generalizable sample of college students who were surveyed pre-pandemic and re-surveyed in May 2020. Method: Items assessed pre-pandemic included DSM-5 AUD and mental health symptoms. A COVID-19 impacts questionnaire was administered, and alcohol and mental health items re-assessed. Results: AUD symptoms decreased from pre-pandemic to during the pandemic, demonstrating a change in trajectory compared to prior cohorts. Students with persistent AUD reported greater concurrent symptoms of PTSD, depression, and alcohol consumption than those with remitted AUD (ps ≤ .02), but not increased COVID-19 impact. Persistent AUD status was predicted by higher sensation seeking and alcohol consumption. Conclusions: Students with concurrent mental health problems are at continued risk for persistent AUD. Findings highlight the impact of the college environment and social context for drinking on AUD.

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.