Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments.

BACKGROUND: We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case-control study (30 per group) with a covariate (case vs control, represented as ß1, set to be null) and two biologically relevant confounding variables (age, represented as ß2, and hemoglobin A1c (HbA1c), represented as ß3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the 'true' dataset (CAPN13 gene). RESULTS: Pre-batch correction, under the null hypothesis (ß1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (ß2 and ß3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses. CONCLUSIONS: Our algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.

DNA Methylation Near DLGAP2 May Mediate the Relationship between Family History of Type 1 Diabetes and Type 1 Diabetes Risk.

Given the differential risk of type 1 diabetes (T1D) in offspring of affected fathers versus affected mothers and our observation that T1D cases have differential DNA methylation near the imprinted DLGAP2 gene compared to controls, we examined whether methylation near DLGAP2 mediates the association between T1D family history and T1D risk. In a nested case-control study of 87 T1D cases and 87 controls from the Diabetes Autoimmunity Study in the Young, we conducted causal mediation analyses at 12 DLGAP2 region CpGs to decompose the effect of family history on T1D risk into indirect and direct effects. These effects were estimated from two regression models adjusted for the human leukocyte antigen DR3/4 genotype: a linear regression of family history on methylation (mediator model) and a logistic regression of family history and methylation on T1D (outcome model). For 8 of the 12 CpGs, we identified a significant interaction between T1D family history and methylation on T1D risk. Accounting for this interaction, we found that the increased risk of T1D for children with affected mothers compared to those with no family history was mediated through differences in methylation at two CpGs (cg27351978, cg00565786) in the DLGAP2 region, as demonstrated by a significant pure natural indirect effect (odds ratio (OR) = 1.98, 95% confidence interval (CI): 1.06-3.71) and nonsignificant total natural direct effect (OR = 1.65, 95% CI: 0.16-16.62) (for cg00565786). In contrast, the increased risk of T1D for children with an affected father or sibling was not explained by DNA methylation changes at these CpGs. Results were similar for cg27351978 and robust in sensitivity analyses. Lastly, we found that DNA methylation in the DLGAP2 region was associated (P<0:05) with gene expression of nearby protein-coding genes DLGAP2, ARHGEF10, ZNF596, and ERICH1. Results indicate that the maternal protective effect conferred through exposure to T1D in utero may operate through changes to DNA methylation that have functional downstream consequences.

Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data.

Omics studies frequently use samples collected during cohort studies. Conditioning on sample availability can cause selection bias if sample availability is nonrandom. Inverse probability weighting (IPW) is purported to reduce this bias. We evaluated IPW in an epigenome-wide analysis testing the association between DNA methylation (261,435 probes) and age in healthy adolescent subjects (n = 114). We simulated age and sex to be correlated with sample selection and then evaluated four conditions: complete population/no selection bias (all subjects), naïve selection bias (no adjustment), and IPW selection bias (selection bias with IPW adjustment). Assuming the complete population condition represented the "truth," we compared each condition to the complete population condition. Bias or difference in associations between age and methylation was reduced in the IPW condition versus the naïve condition. However, genomic inflation and type 1 error were higher in the IPW condition relative to the naïve condition. Postadjustment using bacon, type 1 error and inflation were similar across all conditions. Power was higher under the IPW condition compared with the naïve condition before and after inflation adjustment. IPW methods can reduce bias in genome-wide analyses. Genomic inflation is a potential concern that can be minimized using methods that adjust for inflation.

HDAC Inhibition Reverses Preexisting Diastolic Dysfunction and Blocks Covert Extracellular Matrix Remodeling.

BACKGROUND: Diastolic dysfunction (DD) is associated with the development of heart failure and contributes to the pathogenesis of other cardiac maladies, including atrial fibrillation. Inhibition of histone deacetylases (HDACs) has been shown to prevent DD by enhancing myofibril relaxation. We addressed the therapeutic potential of HDAC inhibition in a model of established DD with preserved ejection fraction. METHODS: Four weeks after uninephrectomy and implantation with deoxycorticosterone acetate pellets, when DD was clearly evident, 1 cohort of mice was administered the clinical-stage HDAC inhibitor ITF2357/Givinostat. Echocardiography, blood pressure measurements, and end point invasive hemodynamic analyses were performed. Myofibril mechanics and intact cardiomyocyte relaxation were assessed ex vivo. Cardiac fibrosis was evaluated by picrosirius red staining and second harmonic generation microscopy of left ventricle (LV) sections, RNA sequencing of LV mRNA, mass spectrometry-based evaluation of decellularized LV biopsies, and atomic force microscopy determination of LV stiffness. Mechanistic studies were performed with primary rat and human cardiac fibroblasts. RESULTS: HDAC inhibition normalized DD without lowering blood pressure in this model of systemic hypertension. In contrast to previous models, myofibril relaxation was unimpaired in uninephrectomy/deoxycorticosterone acetate mice. Furthermore, cardiac fibrosis was not evident in any mouse cohort on the basis of picrosirius red staining or second harmonic generation microscopy. However, mass spectrometry revealed induction in the expression of >100 extracellular matrix proteins in LVs of uninephrectomy/deoxycorticosterone acetate mice, which correlated with profound tissue stiffening based on atomic force microscopy. ITF2357/Givinostat treatment blocked extracellular matrix expansion and LV stiffening. The HDAC inhibitor was subsequently shown to suppress cardiac fibroblast activation, at least in part, by blunting recruitment of the profibrotic chromatin reader protein BRD4 (bromodomain-containing protein 4) to key gene regulatory elements. CONCLUSIONS: These findings demonstrate the potential of HDAC inhibition as a therapeutic intervention to reverse existing DD and establish blockade of extracellular matrix remodeling as a second mechanism by which HDAC inhibitors improve ventricular filling. Our data reveal the existence of pathophysiologically relevant covert or hidden cardiac fibrosis that is below the limit of detection of histochemical stains such as picrosirius red, highlighting the need to evaluate fibrosis of the heart using diverse methodologies.

The oxylipin profile is associated with development of type 1 diabetes: the Diabetes Autoimmunity Study in the Young (DAISY).

AIMS/HYPOTHESIS: Oxylipins are lipid mediators derived from polyunsaturated fatty acids. Some oxylipins are proinflammatory (e.g. those derived from arachidonic acid [ARA]), others are pro-resolving of inflammation (e.g. those derived from α-linolenic acid [ALA], docosahexaenoic acid [DHA] and eicosapentaenoic acid [EPA]) and others may be both (e.g. those derived from linoleic acid [LA]). The goal of this study was to examine whether oxylipins are associated with incident type 1 diabetes. METHODS: We conducted a nested case-control analysis in the Diabetes Autoimmunity Study in the Young (DAISY), a prospective cohort study of children at risk of type 1 diabetes. Plasma levels of 14 ARA-derived oxylipins, ten LA-derived oxylipins, six ALA-derived oxylipins, four DHA-derived oxylipins and two EPA-related oxylipins were measured by ultra-HPLC-MS/MS at multiple timepoints related to autoantibody seroconversion in 72 type 1 diabetes cases and 71 control participants, which were frequency matched on age at autoantibody seroconversion (of the case), ethnicity and sample availability. Linear mixed models were used to obtain an age-adjusted mean of each oxylipin prior to type 1 diabetes. Age-adjusted mean oxylipins were tested for association with type 1 diabetes using logistic regression, adjusting for the high risk HLA genotype HLA-DR3/4,DQB1*0302. We also performed principal component analysis of the oxylipins and tested principal components (PCs) for association with type 1 diabetes. Finally, to investigate potential critical timepoints, we examined the association of oxylipins measured before and after autoantibody seroconversion (of the cases) using PCs of the oxylipins at those visits. RESULTS: The ARA-related oxylipin 5-HETE was associated with increased type 1 diabetes risk. Five LA-related oxylipins, two ALA-related oxylipins and one DHA-related oxylipin were associated with decreased type 1 diabetes risk. A profile of elevated LA- and ALA-related oxylipins (PC1) was associated with decreased type 1 diabetes risk (OR 0.61; 95% CI 0.40, 0.94). A profile of elevated ARA-related oxylipins (PC2) was associated with increased diabetes risk (OR 1.53; 95% CI 1.03, 2.29). A critical timepoint analysis showed type 1 diabetes was associated with a high ARA-related oxylipin profile at post-autoantibody-seroconversion but not pre-seroconversion. CONCLUSIONS/INTERPRETATION: The protective association of higher LA- and ALA-related oxylipins demonstrates the importance of both inflammation promotion and resolution in type 1 diabetes. Proinflammatory ARA-related oxylipins may play an important role once the autoimmune process has begun.

Predictors of oxylipins in a healthy pediatric population.

BACKGROUND: Oxylipins are formed from oxidation of omega-6 (n6) and omega-3 (n3) fatty acids (FAs). Evidence for inflammatory effects comes mostly from adults. METHODS: Oxylipins from n6 FA (27 n6-oxylipins) and n3 FA (12 n3-oxylipins) were measured through ultra-high-performance liquid chromatography-mass spectrometry (LC-MS/MS) in plasma from 111 children at risk of type 1 diabetes (age 1-17 years) studied longitudinally. Oxylipin precursor FAs (arachidonic acid, linoleic acid, alpha-linolenic acid, docosahexaenoic acid, eicosapentaenoic acid) were measured in red blood cell (RBC) membrane and plasma. Precursor FAs dietary intake was measured through food frequency questionnaire and environmental tobacco smoke (ETS) through questionnaires. Linear mixed models were used to test oxylipins with predictors. RESULTS: Age associated with 15 n6- and 6 n3-oxylipins; race/ethnicity associated with 3 n6- and 1 n3-oxylipins; sex associated with 2 n6-oxylipins. ETS associated with lipoxin-A4. Oxylipins associated with precursor FAs in plasma more often than RBC. RBC levels and dietary intake of precursor FAs more consistently associated with n3-oxylipins than with n6-oxylipins. CONCLUSIONS: In healthy children, oxylipin levels change with age. Oxylipins associated with precursor FAs more often in plasma than RBC or diet, suggesting that inflammatory regulation leading to FA release into plasma may also be a determinant of oxylipin generation. IMPACT: This is the first study to examine predictors of oxylipins in healthy children at risk of type 1 diabetes. In healthy children at risk of type 1 diabetes, many oxylipins change with age, and most oxylipins do not differ by sex or race/ethnicity. Environmental tobacco smoke exposure was associated with the presence of lipoxin A4. Omega-6- and omega-3-related oxylipin levels were consistently associated with their respective precursor fatty acid levels measured in the plasma. Proportionally more omega-3 compared to omega-6 oxylipins were associated with dietary intake and red blood cell membrane levels of the respective precursor fatty acid.

Dietary factors during pregnancy and atopic outcomes in childhood: A systematic review from the European Academy of Allergy and Clinical Immunology.

RATIONALE: Allergic diseases are an increasing public health concern, and early life environment is critical to immune development. Maternal diet during pregnancy has been linked to offspring allergy risk. In turn, maternal diet is a potentially modifiable factor, which could be targeted as an allergy prevention strategy. In this systematic review, we focused on non-allergen-specific modifying factors of the maternal diet in pregnancy on allergy outcomes in their offspring. METHODS: We undertook a systematic review of studies investigating the association between maternal diet during pregnancy and allergic outcomes (asthma/wheeze, hay fever/allergic rhinitis/seasonal allergies, eczema/atopic dermatitis (AD), food allergies, and allergic sensitization) in offspring. Studies evaluating the effect of food allergen intake were excluded. We searched three bibliographic databases (MEDLINE, EMBASE, and Web of Science) through February 26, 2019. Evidence was critically appraised using modified versions of the Cochrane Collaboration Risk of Bias tool for intervention trials and the National Institute for Clinical Excellence methodological checklist for cohort and case-control studies and meta-analysis performed from RCTs. RESULTS: We identified 95 papers: 17 RCTs and 78 observational (case-control, cross-sectional, and cohort) studies. Observational studies varied in design and dietary intakes and often had contradictory findings. Based on our meta-analysis, RCTs showed that vitamin D supplementation (OR: 0.72; 95% CI: 0.56-0.92) is associated with a reduced risk of wheeze/asthma. A positive trend for omega-3 fatty acids was observed for asthma/wheeze, but this did not reach statistical significance (OR: 0.70; 95% CI: 0.45-1.08). Omega-3 supplementation was also associated with a non-significant decreased risk of allergic rhinitis (OR: 0.76; 95% CI: 0.56-1.04). Neither vitamin D nor omega-3 fatty acids were associated with an altered risk of AD or food allergy. CONCLUSIONS: Prenatal supplementation with vitamin D may have beneficial effects for prevention of asthma. Additional nutritional factors seem to be required for modulating the risk of skin and gastrointestinal outcomes. We found no consistent evidence regarding other dietary factors, perhaps due to differences in study design and host features that were not considered. While confirmatory studies are required, there is also a need for performing RCTs beyond single nutrients/foods.

Metabolomics-related nutrient patterns at seroconversion and risk of progression to type 1 diabetes.

OBJECTIVE: Our aim was to elucidate the role of diet in type 1 diabetes (T1D) by examining combinations of nutrient intake in the progression from islet autoimmunity (IA) to T1D. METHODS: We measured 2457 metabolites and dietary intake at the time of seroconversion in 132 IA-positive children in the prospective Diabetes Autoimmunity Study in the Young. IA was defined as the first of two consecutive visits positive for at least one autoantibody (insulin, GAD, IA-2, or ZnT8). By December 2018, 40 children progressed to T1D. Intakes of 38 nutrients were estimated from semiquantitative food frequency questionnaires. We tested the association of each metabolite with progression to T1D using multivariable Cox regression. Nutrient patterns that best explained variation in candidate metabolites were identified using reduced rank regression (RRR), and their association with progression to T1D was tested using Cox regression adjusting for age at seroconversion and high-risk HLA genotype. RESULTS: In stepwise selection, 22 nutrients significantly predicted at least two of the 13 most significant metabolites associated with progression to T1D, and were included in RRR. A nutrient pattern corresponding to intake lower in linoleic acid, niacin, and riboflavin, and higher in total sugars, explained 18% of metabolite variability. Children scoring higher on this metabolite-related nutrient pattern at seroconversion had increased risk for progressing to T1D (HR = 3.17, 95%CI = 1.42-7.05). CONCLUSIONS: Combinations of nutrient intake reflecting candidate metabolites are associated with increased risk of T1D, and may help focus dietary prevention efforts.

DNA methylation near the INS gene is associated with INS genetic variation (rs689) and type 1 diabetes in the Diabetes Autoimmunity Study in the Young.

OBJECTIVE: Mechanisms underlying the role of non-human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non-HLA genetic risk. METHODS: We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case-control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency-matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non-HLA genetic markers known be associated with T1D. Genome-wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) -probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta-analysis was used to combine odds ratios from the two platforms. RESULTS: We identified 10 SNP-methylation probe pairs (false discovery rate (FDR) adjusted P < .05 and validation P < .05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta-P = .0495), cg21574853 (OR: 2.5, meta-P = .0232), and cg25336198 (odds ratio: 6.6, meta-P = .0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site. CONCLUSIONS: We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development.

Voluntary exposure to a toxin: the genetic influence on ethanol consumption.

Ethyl alcohol is a toxin that, when consumed at high levels, produces organ damage and death. One way to prevent or ameliorate this damage in humans is to reduce the exposure of organs to alcohol by reducing alcohol ingestion. Both the propensity to consume large volumes of alcohol and the susceptibility of human organs to alcohol-induced damage exhibit a strong genetic influence. We have developed an integrative genetic/genomic approach to identify transcriptional networks that predispose complex traits, including propensity for alcohol consumption and propensity for alcohol-induced organ damage. In our approach, the phenotype is assessed in a panel of recombinant inbred (RI) rat strains, and quantitative trait locus (QTL) analysis is performed. Transcriptome data from tissues/organs of naïve RI rat strains are used to identify transcriptional networks using Weighted Gene Coexpression Network Analysis (WGCNA). Correlation of the first principal component of transcriptional coexpression modules with the phenotype across the rat strains, and overlap of QTLs for the phenotype and the QTLs for the coexpression modules (module eigengene QTL) provide the criteria for identification of the functionally related groups of genes that contribute to the phenotype (candidate modules). While we previously identified a brain transcriptional module whose QTL overlapped with a QTL for levels of alcohol consumption in HXB/BXH RI rat strains and 12 selected rat lines, this module did not account for all of the genetic variation in alcohol consumption. Our search for QTL overlap and correlation of coexpression modules with phenotype can, however, be applied to any organ in which the transcriptome has been measured, and this represents a holistic approach in the search for genetic contributors to complex traits. Previous work has implicated liver/brain interactions, particularly involving inflammatory/immune processes, as influencing alcohol consumption levels. We have now analyzed the liver transcriptome of the HXB/BXH RI rat panel in relation to the behavioral trait of alcohol consumption. We used RNA-Seq and microarray data to construct liver transcriptional networks, and identified a liver candidate transcriptional coexpression module that explained 24% of the genetic variance in voluntary alcohol consumption. The transcripts in this module focus attention on liver secretory products that influence inflammatory and immune signaling pathways. We propose that these liver secretory products can interact with brain mechanisms that affect alcohol consumption, and targeting these pathways provides a potential approach to reducing high levels of alcohol intake and also protecting the integrity of the liver and other organs.

Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism.

BACKGROUND: A statistical pipeline was developed and used for determining candidate genes and candidate gene coexpression networks involved in 2 alcohol (i.e., ethanol [EtOH]) metabolism phenotypes, namely alcohol clearance and acetate area under the curve in a recombinant inbred (RI) (HXB/BXH) rat panel. The approach was also used to provide an indication of how EtOH metabolism can impact the normal function of the identified networks. METHODS: RNA was extracted from alcohol-naïve liver tissue of 30 strains of HXB/BXH RI rats. The reconstructed transcripts were quantitated, and data were used to construct gene coexpression modules and networks. A separate group of rats, comprising the same 30 strains, were injected with EtOH (2 g/kg) for measurement of blood EtOH and acetate levels. These data were used for quantitative trait loci (QTL) analysis of the rate of EtOH disappearance and circulating acetate levels. The analysis pipeline required calculation of the module eigengene values, the correction of these values with EtOH metabolism rates and acetate levels across the rat strains, and the determination of the eigengene QTLs. For a module to be considered a candidate for determining phenotype, the module eigengene values had to have significant correlation with the strain phenotypic values and the module eigengene QTLs had to overlap the phenotypic QTLs. RESULTS: Of the 658 transcript coexpression modules generated from liver RNA sequencing data, a single module satisfied all criteria for being a candidate for determining the alcohol clearance trait. This module contained 2 alcohol dehydrogenase genes, including the gene whose product was previously shown to be responsible for the majority of alcohol elimination in the rat. This module was also the only module identified as a candidate for influencing circulating acetate levels. This module was also linked to the process of generation and utilization of retinoic acid as related to the autonomous immune response. CONCLUSIONS: We propose that our analytical pipeline can successfully identify genetic regions and transcripts which predispose a particular phenotype and our analysis provides functional context for coexpression module components.

Influence of sex on genetic regulation of "drinking in the dark" alcohol consumption.

The ILSXISS (LXS) recombinant inbred (RI) panel of mice is a valuable resource for genetic mapping studies of complex traits, due to its genetic diversity and large number of strains. Male and female mice from this panel were used to investigate genetic influences on alcohol consumption in the "drinking in the dark" (DID) model. Male mice (38 strains) and female mice (36 strains) were given access to 20% ethanol during the early phase of their circadian dark cycle for four consecutive days. The first principal component of alcohol consumption measures on days 2, 3, and 4 was used as a phenotype (DID phenotype) to calculate QTLs, using a SNP marker set for the LXS RI panel. Five QTLs were identified, three of which included a significant genotype by sex interaction, i.e., a significant genotype effect in males and not females. To investigate candidate genes associated with the DID phenotype, data from brain microarray analysis (Affymetrix Mouse Exon 1.0 ST Arrays) of male LXS RI strains were combined with RNA-Seq data (mouse brain transcriptome reconstruction) from the parental ILS and ISS strains in order to identify expressed mouse brain transcripts. Candidate genes were determined based on common eQTL and DID phenotype QTL regions and correlation of transcript expression levels with the DID phenotype. The resulting candidate genes (in particular, Arntl/Bmal1) focused attention on the influence of circadian regulation on the variation in the DID phenotype in this population of mice.

Is the alcohol deprivation effect genetically mediated? Studies with HXB/BXH recombinant inbred rat strains.

BACKGROUND: Two features of alcohol addiction that have been widely studied in animal models are relapse drinking following periods of alcohol abstinence and the escalation of alcohol consumption after chronic continuous or intermittent alcohol exposure. The genetic contribution to these phenotypes has not been systematically investigated. METHODS: HXB/BXH recombinant inbred (RI) rat strains were given access to alcohol sequentially as follows: alcohol (10%) as the only fluid for 1 week; alcohol (10%) and water in a 2-bottle choice paradigm for 7 weeks ("pre-alcohol deprivation effect [ADE] alcohol consumption"); 2 weeks of access to water only (alcohol deprivation); and 2 weeks of reaccess to 10% alcohol and water ("post-ADE alcohol consumption"). The periods of deprivation and reaccess to alcohol were repeated 3 times. The ADE was defined as the amount of alcohol consumed in the first 24 hours after deprivation minus the average daily amount of alcohol consumed in the week prior to deprivation. Heritability of the phenotypes was determined by analysis of variance, and quantitative trait loci (QTLs) were identified. RESULTS: All strains showed increased alcohol consumption, compared to the predeprivation period, in the first 24 hours after each deprivation (ADE). Broad-sense heritability of the ADEs was low (ADE1, 9.1%; ADE2, 26.2%; ADE3, 16.3%). Alcohol consumption levels were relatively stable over weeks 2 to 7. Post-ADE alcohol consumption levels consistently increased in some strains and were decreased or unchanged in others. Heritability of pre- and post-ADE alcohol consumption was high and increased over time (week 2, 38.5%; week 7, 51.1%; week 11, 56.8%; week 15, 63.3%). QTLs for pre- and post-ADE alcohol consumption were similar, but the strength of the QTL association with the phenotype decreased over time. CONCLUSIONS: In the HXB/BXH RI rat strains, genotypic variance does not account for a large proportion of phenotypic variance in the ADE phenotype (low heritability), suggesting a role of environmental factors. In contrast, a large proportion of the variance across the RI strains in pre- and post-ADE alcohol consumption is due to genetically determined variance (high heritability).

Genome-wide association discoveries of alcohol dependence.

OBJECTIVE: To report the genome-wide significant and/or replicable risk variants for alcohol dependence and explore their potential biological functions. METHODS: We searched in PubMed for all genome-wide association studies (GWASs) of alcohol dependence. The following three types of the results were extracted: genome-wide significant associations in an individual sample, the combined samples, or the meta-analysis (p < 5 × 10(-8) ); top-ranked associations in an individual sample (p < 10(-5) ) that were nominally replicated in other samples (p < .05); and nominally replicable associations across at least three independent GWAS samples (p < .05). These results were meta-analyzed. cis-eQTLs in human, RNA expression in rat and mouse brains and bioinformatics properties of all of these risk variants were analyzed. RESULTS: The variants located within the alcohol dehydrogenase (ADH) cluster were significantly associated with alcohol dependence at the genome-wide level (p < 5 × 10(-8) ) in at least one sample. Some associations with the ADH cluster were replicable across six independent GWAS samples. The variants located within or near SERINC2, KIAA0040, MREG-PECR or PKNOX2 were significantly associated with alcohol dependence at the genome-wide level (p < 5 × 10(-8) ) in meta-analysis or combined samples, and these associations were replicable across at least one sample. The associations with the variants within NRD1, GPD1L-CMTM8 or MAP3K9-PCNX were suggestive (5 × 10(-8) < p < 10(-5) ) in some samples, and nominally replicable in other samples. The associations with the variants at HTR7 and OPA3 were nominally replicable across at least three independent GWAS samples (10(-5) < p < .05). Some risk variants at the ADH cluster, SERINC2, KIAA0040, NRD1, and HTR7 had potential biological functions. CONCLUSION: The most robust risk locus was the ADH cluster. SERINC2, KIAA0040, NRD1, and HTR7 were also likely to play important roles in alcohol dependence. PKNOX2, MREG, PECR, GPD1L, CMTM8, MAP3K9, PCNX, and OPA3 might play less important roles in risk for alcohol dependence based on the function analysis. This conclusion will significantly contribute to the post-GWAS follow-up studies on alcohol dependence.

Genome-Wide Association Study and transcriptome analysis reveals a complex gene network that regulates opsin gene expression and cell fate determination in Drosophila R7 photoreceptor cells.

Background: An animal's ability to discriminate between differing wavelengths of light (i.e., color vision) is mediated, in part, by a subset of photoreceptor cells that express opsins with distinct absorption spectra. In Drosophila R7 photoreceptors, expression of the rhodopsin molecules, Rh3 or Rh4, is determined by a stochastic process mediated by the transcription factor spineless. The goal of this study was to identify additional factors that regulate R7 cell fate and opsin choice using a Genome Wide Association Study (GWAS) paired with transcriptome analysis via RNA-Seq. Results: We examined Rh3 and Rh4 expression in a subset of fully-sequenced inbred strains from the Drosophila Genetic Reference Panel and performed a GWAS to identify 42 naturally-occurring polymorphisms-in proximity to 28 candidate genes-that significantly influence R7 opsin expression. Network analysis revealed multiple potential interactions between the associated candidate genes, spineless and its partners. GWAS candidates were further validated in a secondary RNAi screen which identified 12 lines that significantly reduce the proportion of Rh3 expressing R7 photoreceptors. Finally, using RNA-Seq, we demonstrated that all but four of the GWAS candidates are expressed in the pupal retina at a critical developmental time point and that five are among the 917 differentially expressed genes in sevenless mutants, which lack R7 cells. Conclusions: Collectively, these results suggest that the relatively simple, binary cell fate decision underlying R7 opsin expression is modulated by a larger, more complex network of regulatory factors. Of particular interest are a subset of candidate genes with previously characterized neuronal functions including neurogenesis, neurodegeneration, photoreceptor development, axon growth and guidance, synaptogenesis, and synaptic function.

A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records.

Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.

Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity.

Background: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers). Methods: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7). Results: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions. Conclusion: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Hippocampal RNA sequencing in mice selectively bred for high and low activity.

High and Low Activity strains of mice were bidirectionally selected for differences in open-field activity (DeFries et al., 1978, Behavior Genetics, 8: 3-13) and subsequently inbred to use as a genetic model for studying anxiety-like behaviors (Booher et al., 2021, Genes, Brain and Behavior, 20: e12730). Hippocampal RNA-sequencing of the High and Low Activity mice identified 3901 differentially expressed protein-coding genes, with both sex-dependent and sex-independent effects. Functional enrichment analysis (PANTHER) highlighted 15 gene ontology terms, which allowed us to create a narrow list of 264 top candidate genes. Of the top candidate genes, 46 encoded four Complexes (I, II, IV and V) and two electron carriers (cytochrome c and ubiquinone) of the mitochondrial oxidative phosphorylation process. The most striking results were in the female high anxiety, Low Activity mice, where 39/46 genes relating to oxidative phosphorylation were upregulated. In addition, comparison of our top candidate genes with two previously curated High and Low Activity gene lists highlight 24 overlapping genes, where Ndufa13, which encodes the supernumerary subunit A13 of complex I, was the only gene to be included in all three lists. Mitochondrial dysfunction has recently been implicated as both a cause and effect of anxiety-related disorders and thus should be further explored as a possible novel pharmaceutical treatment for anxiety disorders.

An Oxylipin-Related Nutrient Pattern and Risk of Type 1 Diabetes in the Diabetes Autoimmunity Study in the Young (DAISY).

Oxylipins, pro-inflammatory and pro-resolving lipid mediators, are associated with the risk of type 1 diabetes (T1D) and may be influenced by diet. This study aimed to develop a nutrient pattern related to oxylipin profiles and test their associations with the risk of T1D among youth. The nutrient patterns were developed with a reduced rank regression in a nested case-control study (n = 335) within the Diabetes Autoimmunity Study in the Young (DAISY), a longitudinal cohort of children at risk of T1D. The oxylipin profiles (adjusted for genetic predictors) were the response variables. The nutrient patterns were tested in the case-control study (n = 69 T1D cases, 69 controls), then validated in the DAISY cohort using a joint Cox proportional hazards model (n = 1933, including 81 T1D cases). The first nutrient pattern (NP1) was characterized by low beta cryptoxanthin, flavanone, vitamin C, total sugars and iron, and high lycopene, anthocyanidins, linoleic acid and sodium. After adjusting for T1D family history, the HLA genotype, sex and race/ethnicity, NP1 was associated with a lower risk of T1D in the nested case-control study (OR: 0.44, p = 0.0126). NP1 was not associated with the risk of T1D (HR: 0.54, p-value = 0.1829) in the full DAISY cohort. Future studies are needed to confirm the nested case-control findings and investigate the modifiable factors for oxylipins.

COVID-19 Mortality in the Colorado Center for Personalized Medicine Biobank.

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.