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In this study, we explored potential associations among self-injurious behaviors (SIB) and a diverse group of protective and risk factors in children with autism spectrum disorder from two databases: Autism and Developmental Disabilities Monitoring (ADDM) Network and the Autism Speaks-Autism Treatment Network (AS-ATN). The presence of SIB was determined from children's records in ADDM and a parent questionnaire in AS-ATN. We used multiple imputation to account for missing data and a non-linear mixed model with site as a random effect to test for associations. Despite differences between the two databases, similar associations were found; SIB were associated with developmental, behavioral, and somatic factors. Implications of these findings are discussed in relation to possible etiology, future longitudinal studies, and clinical practice.

In this study, we explored potential associations among self-injurious behaviors (SIB) and a diverse group of protective and risk factors in children with autism spectrum disorder from two databases: Autism and Developmental Disabilities Monitoring (ADDM) Network and the Autism Speaks-Autism Treatment Network (AS-ATN). The presence of SIB was determined from children's records in ADDM and a parent questionnaire in AS-ATN. We used multiple imputation to account for missing data and a non-linear mixed model with site as a random effect to test for associations. Despite differences between the two databases, similar associations were found; SIB were associated with developmental, behavioral, and somatic factors. Implications of these findings are discussed in relation to possible etiology, future longitudinal studies, and clinical practice., Author(s): G. N. Soke [sup.1] [sup.2] , S. A. Rosenberg [sup.3] , R. F. Hamman [sup.1] , T. Fingerlin [sup.1] , C. R. Rosenberg [sup.3] [sup.4] , L. Carpenter [sup.5] [...]

Abstract: Aims: To identify gestational diabetes mellitus exposure‐associated DNA methylation changes and assess whether such changes are also associated with adiposity‐related outcomes. Methods: We performed an epigenome‐wide association analysis, using Illumina 450k methylation arrays, on whole blood collected, on average, at 10.5 years of age from 81 gestational diabetes‐exposed and 81 unexposed offspring enrolled in the EPOCH (Exploring Perinatal Outcomes in Children) study, and on the cord blood of 31 gestational diabetes‐exposed and 64 unexposed offspring enrolled in the Colorado Healthy Start cohort. Validation was performed by pyrosequencing. Results: We identified 98 differentially methylated positions associated with gestational diabetes exposure at a false discovery rate of <10% in peripheral blood, with 51 loci remaining significant (plus additional 40 loci) after adjustment for cell proportions. We also identified 2195 differentially methylation regions at a false discovery rate of <5% after adjustment for cell proportions. We prioritized loci for pyrosequencing validation and association analysis with adiposity‐related outcomes based on strengths of association and effect size, network and pathway analysis, analysis of cord blood, and previous publications. Methylation in six out of nine (67%) gestational diabetes‐associated genes was validated and we also showed that methylation of SH3PXD2A was significantly (P<0.05) associated with multiple adiposity‐related outcomes. Conclusions: Our findings suggest that epigenetic marks may provide an important link between in utero exposure to gestational diabetes and obesity in childhood, and add to the growing body of evidence that DNA methylation is affected by gestational diabetes exposure. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a complex systemic disease with clinical heterogeneity based on varying phenotypes and natural history. The detailed etiology of sarcoidosis remains unknown, but genetic predisposition as well as environmental exposures play a significant role in disease pathogenesis. We performed a comprehensive review of germline genetic (DNA) and transcriptomic (RNA) studies of sarcoidosis, including both previous studies and more recent findings. In this review, we provide an assessment of the following: genetic variants in sarcoidosis susceptibility and phenotypes, ancestry- and sex-specific genetic variants in sarcoidosis, shared genetic architecture between sarcoidosis and other diseases, and gene-environment interactions in sarcoidosis. We also highlight the unmet needs in sarcoidosis genetic studies, including the pressing requirement to include diverse populations and have consistent definitions of phenotypes in the sarcoidosis research community to help advance the application of genetic predisposition to sarcoidosis disease risk and manifestations., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded., (© 2023. Springer Nature Limited.)

Background/aim: Discontinuation of aspirin (ASA) prior to elective craniotomies is common practice. However, patients treated with ASA for secondary prevention bear a higher risk for thromboembolic complications. Aim of this systematic review is to investigate the risks and benefits of perioperative continuation and discontinuation of ASA in elective craniotomies., Methods: PubMed and Embase databases were searched. Inclusion criteria were retro- and prospective studies, reporting hemorrhagic and thromboembolic complications in patients in whom ASA was either continued or discontinued perioperatively in elective craniotomies. We excluded shunt operations and emergency cases. The MINORS (Methodological index for non-randomized studies) score was used to quantify the methodological quality of the eligible studies., Results: Out of 523 publications, 7 met the eligibility criteria (cumulative cohort of 646 patients). The mean MINORS score for the comparative studies was 18.7/24 (± SD 2.07, range: 17-22) and 9/16 for the unique non-comparative study, indicating an overall weak methodological quality of the included studies. 57.1% of the patients underwent craniotomy for intra- and extra-axial tumor removal, 39.0% for bypass surgery and 3.9% for neurovascular lesions (other than bypass). In 31.0% of the cases, ASA was prescribed for primary and in 69.0% for secondary prevention. ASA was continued perioperatively in 61.8% and discontinued in 38.2% of the cases. The hemorrhagic complication rate was 3% (95% CI [0.01-0.05]) in the ASA continuation group (Con-Group) and 3% (95% CI [0.01-0.09]) in the discontinuation group (Disc-Group) (p = 0.9). The rate of thromboembolic events in the Con-Group was 3% (95% CI [0.01-0.06]) in comparison to 6% (95% CI [0.02-0.14]) in the Disc-Group (p = 0.1)., Conclusion: Perioperative continuation of ASA in elective craniotomies does not seem to be associated with an increased hemorrhagic risk. The potential beneficial effect of ASA continuation on thromboembolic events needs to be further investigated in patients under ASA for secondary prevention., (© 2022. The Author(s).)

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Background: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference., Results: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements., Conclusions: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450 K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. Change in methylation with age differed between cases and controls in 10 regions. Average longitudinal methylation differed between cases and controls at two genomic positions and 28 regions. Some methylation differences were detectable and consistent as early as birth, including before and after the onset of preclinical islet autoimmunity. Results map to transcription factors, other protein coding genes, and non-coding regions of the genome with regulatory potential. The identification of methylation differences that predate islet autoimmunity and clinical diagnosis may suggest a role for epigenetics in T1D pathogenesis; however, functional validation is warranted.

Background: Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT., Methods: First-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed., Findings: In 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex., Interpretation: PrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities., Competing Interests: Competing interests: DAS is the founder and chief scientific officer of Eleven P15, a company focused on the early diagnosis and treatment of pulmonary fibrosis. DAS has an awarded patent (US patent no: 8,673,565) for the treatment and diagnosis of fibrotic lung disease. DAL and SMH have a pending patent (application US20170330320A1) for image analysis; SMH reports a consulting agreement with Boehringer Ingelheim., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Expression quantitative trait loci (eQTL) provide insight on transcription regulation and illuminate the molecular basis of phenotypic outcomes. High-throughput RNA sequencing (RNA-seq) is becoming a popular technique to measure gene expression abundance. Traditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution. As a result, for RNA-seq data, total read count measurements can be normalized by normal quantile transformation in order to fit the data using a linear regression. Other approaches model the total read counts using a negative binomial regression. While these methods work well for common variants (minor allele frequencies > 5% or 1%), an extension of existing methodology is needed to accommodate a collection of rare variants in RNA-seq data. Here, we examine 2 approaches that are direct applications of existing methodology and apply these approaches to RNAseq studies: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these approaches via simulation studies under numerous scenarios and applied these approaches to the 1,000 Genomes Project., Competing Interests: The authors have declared that no competing interests exist.

Pulmonary emphysema is a destructive disease of the lungs that is currently diagnosed via visual assessment of lung Computed Tomography (CT) scans by a radiologist. Visual assessment can have poor inter-rater reliability, is time consuming, and requires access to trained assessors. Quantitative methods that reliably summarize the biologically relevant characteristics of an image are needed to improve the way lung diseases are characterized. The goal of this work was to show how spatial point process models can be used to create a set of radiologically derived quantitative lung biomarkers of emphysema. We formalized a general framework for applying spatial point processes to lung CT scans, and developed a Shot Noise Cox Process to quantify how radiologically based emphysematous tissue clusters into larger structures. Bayesian estimation of model parameters was done using spatial Birth-Death MCMC (BD-MCMC). In simulations, we showed the BD-MCMC estimation algorithm is able to accurately recover model parameters. In an application to real lung CT scans from the COPDGene cohort, we showed variability in the clustering characteristics of emphysematous tissue across disease subtypes that were based on visual assessments of the CT scans.

Background: Given the phenotypic similarities between rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) (hereafter, RA-ILD) and idiopathic pulmonary fibrosis, we hypothesized that the strongest risk factor for the development of idiopathic pulmonary fibrosis, the gain-of-function MUC5B promoter variant rs35705950, would also contribute to the risk of ILD among patients with RA., Methods: Using a discovery population and multiple validation populations, we tested the association of the MUC5B promoter variant rs35705950 in 620 patients with RA-ILD, 614 patients with RA without ILD, and 5448 unaffected controls., Results: Analysis of the discovery population revealed an association of the minor allele of the MUC5B promoter variant with RA-ILD when patients with RA-ILD were compared with unaffected controls (adjusted odds ratio, 3.8; 95% confidence interval [CI], 2.8 to 5.2; P=9.7×10 -17 ). The MUC5B promoter variant was also significantly overrepresented among patients with RA-ILD, as compared with unaffected controls, in an analysis of the multiethnic case series (adjusted odds ratio, 5.5; 95% CI, 4.2 to 7.3; P=4.7×10 -35 ) and in a combined analysis of the discovery population and the multiethnic case series (adjusted odds ratio, 4.7; 95% CI, 3.9 to 5.8; P=1.3×10 -49 ). In addition, the MUC5B promoter variant was associated with an increased risk of ILD among patients with RA (adjusted odds ratio in combined analysis, 3.1; 95% CI, 1.8 to 5.4; P=7.4×10 -5 ), particularly among those with evidence of usual interstitial pneumonia on high-resolution computed tomography (adjusted odds ratio in combined analysis, 6.1; 95% CI, 2.9 to 13.1; P=2.5×10 -6 ). However, no significant association with the MUC5B promoter variant was observed for the diagnosis of RA alone., Conclusions: We found that the MUC5B promoter variant was associated with RA-ILD and more specifically associated with evidence of usual interstitial pneumonia on imaging. (Funded by Société Française de Rhumatologie and others.).

This corrects the article DOI: 10.1038/sdata.2017.179.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants., Competing Interests: Conflict of interest statement: S.E.L., J. Laramie, and R.G.T. were employees of Complete Genomics during this study. T.M.T. is an employee of Regeneron Pharmaceuticals. D.A. is an employee of Vertex Pharmaceuticals.

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2 ., (© 2017 by the American Diabetes Association.)

Importance: The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation., Objective: To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases., Data Sources: Genomewide association studies (GWAS) published up to January 15, 2015., Study Selection: GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available., Data Extraction and Synthesis: Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population., Main Outcomes and Measures: Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation., Results: Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15])., Conclusions and Relevance: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Self-injurious behaviors (SIB) have been reported in more than 30 % of children with an autism spectrum disorder (ASD) in clinic-based studies. This study estimated the prevalence of SIB in a large population-based sample of children with ASD in the United States. A total of 8065 children who met the surveillance case definition for ASD in the Autism and Developmental Disabilities Monitoring (ADDM) Network during the 2000, 2006, and 2008 surveillance years were included. The presence of SIB was reported from available health and/or educational records by an expert clinician in ADDM Network. SIB prevalence averaged 27.7 % across all sites and surveillance years, with some variation between sites. Clinicians should inquire about SIB during assessments of children with ASD.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Type 2 diabetes (T2D) is a heterogeneous disorder with contributions from peripheral insulin resistance and β-cell dysfunction. For minimization of phenotypic heterogeneity, quantitative intermediate phenotypes characterizing basal glucose homeostasis (insulin resistance and HOMA of insulin resistance [HOMAIR] and of β-cell function [HOMAB]) have shown promise in relatively large samples. We investigated the utility of dynamic measures of glucose homeostasis (insulin sensitivity [SI] and acute insulin response [AIRg]) evaluating T2D-susceptibility variants (n = 57) in Hispanic Americans from the GUARDIAN Consortium (n = 2,560). Basal and dynamic measures were genetically correlated (HOMAB-AIRg: ρG = 0.28-0.73; HOMAIR-SI: ρG = -0.73 to -0.83) with increased heritability for the dynamic measure AIRg Significant association of variants with dynamic measures (P < 8.77 × 10(-4)) was observed. A pattern of superior performance of AIRg was observed for well-established loci including MTNR1B (P = 9.46 × 10(-12)), KCNQ1 (P = 1.35 × 10(-4)), and TCF7L2 (P = 5.10 × 10(-4)) with study-wise statistical significance. Notably, significant association of MTNR1B with AIRg (P < 1.38 × 10(-9)) was observed in a population one-fourteenth the size of the initial discovery cohort. These observations suggest that basal and dynamic measures provide different views and levels of sensitivity to discrete elements of glucose homeostasis. Although more costly to obtain, dynamic measures yield significant results that could be considered physiologically "closer" to causal pathways and provide insight into the discrete mechanisms of action., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Epidemiologic simulation modeling of highly pathogenic avian influenza (HPAI) outbreaks provides a useful conceptual framework with which to estimate the consequences of HPAI outbreaks and to evaluate disease control strategies. The purposes of this study were to establish detailed and informed input parameters for an epidemiologic simulation model of the H5N1 strain of HPAI among commercial and backyard poultry in the state of South Carolina in the United States using a highly realistic representation of this poultry population; to estimate the consequences of an outbreak of HPAI in this population with a model constructed from these parameters; and to briefly evaluate the sensitivity of model outcomes to several parameters. Parameters describing disease state durations; disease transmission via direct contact, indirect contact, and local-area spread; and disease detection, surveillance, and control were established through consultation with subject matter experts, a review of the current literature, and the use of several computational tools. The stochastic model constructed from these parameters produced simulated outbreaks ranging from 2 to 111 days in duration (median 25 days), during which 1 to 514 flocks were infected (median 28 flocks). Model results were particularly sensitive to the rate of indirect contact that occurs among flocks. The baseline model established in this study can be used in the future to evaluate various control strategies, as a tool for emergency preparedness and response planning, and to assess the costs associated with disease control and the economic consequences of a disease outbreak., (Published by Elsevier B.V.)

In an attempt to discover and unravel genetic predisposition to complex traits, new statistical methods have emerged that utilize multiple sources of data. This appeal to data aggregation is seen on various levels: across genetic variants, across genomic/biological/environmental measures and across different studies, often with fundamentally differing designs. While combining data can increase power to detect genetic variants associated with disease phenotypes, care must be taken in the design, analysis, and interpretation of such studies. Here, we explore methodologies employed to combine sources of genetic data and discuss the prospects for novel advances in the fields of statistical genetics and genetic epidemiology.

The present study investigated the association between variants in the vitamin D receptor gene (VDR) and protein tyrosine phosphatase, non-receptor type 2 gene (PTPN2), as well as an interaction between VDR and PTPN2 and the risk of islet autoimmunity (IA) and progression to type 1 diabetes (T1D). The Diabetes Autoimmunity Study in the Young (DAISY) has followed children at increased risk of T1D since 1993. Of the 1692 DAISY children genotyped for VDR rs1544410, VDR rs2228570, VDR rs11568820, PTPN2 rs1893217, and PTPN2 rs478582, 111 developed IA, defined as positivity for GAD, insulin or IA-2 autoantibodies on 2 or more consecutive visits, and 38 IA positive children progressed to T1D. Proportional hazards regression analyses were conducted. There was no association between IA development and any of the gene variants, nor was there evidence of a VDR*PTPN2 interaction. Progression to T1D in IA positive children was associated with the VDR rs2228570 GG genotype (HR: 0.49, 95% CI: 0.26-0.92) and there was an interaction between VDR rs1544410 and PTPN2 rs1893217 (p(interaction)=0.02). In children with the PTPN2 rs1893217 AA genotype, the VDR rs1544410 AA/AG genotype was associated with a decreased risk of T1D (HR: 0.24, 95% CI: 0.11-0.53, p=0.0004), while in children with the PTPN2 rs1893217 GG/GA genotype, the VDR rs1544410 AA/AG genotype was not associated with T1D (HR: 1.32, 95% CI: 0.43-4.06, p=0.62). These findings should be replicated in larger cohorts for confirmation. The interaction between VDR and PTPN2 polymorphisms in the risk of progression to T1D offers insight concerning the role of vitamin D in the etiology of T1D., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

DUF1220 domains show the largest human-lineage-specific increase in copy number of any protein-coding region in the human genome and map primarily to 1q21, where deletions and reciprocal duplications have been associated with microcephaly and macrocephaly, respectively. Given these findings and the high correlation between DUF1220 copy number and brain size across primate lineages (R(2) = 0.98; p = 1.8 × 10(-6)), DUF1220 sequences represent plausible candidates for underlying 1q21-associated brain-size pathologies. To investigate this possibility, we used specialized bioinformatics tools developed for scoring highly duplicated DUF1220 sequences to implement targeted 1q21 array comparative genomic hybridization on individuals (n = 42) with 1q21-associated microcephaly and macrocephaly. We show that of all the 1q21 genes examined (n = 53), DUF1220 copy number shows the strongest association with brain size among individuals with 1q21-associated microcephaly, particularly with respect to the three evolutionarily conserved DUF1220 clades CON1(p = 0.0079), CON2 (p = 0.0134), and CON3 (p = 0.0116). Interestingly, all 1q21 DUF1220-encoding genes belonging to the NBPF family show significant correlations with frontal-occipital-circumference Z scores in the deletion group. In a similar survey of a nondisease population, we show that DUF1220 copy number exhibits the strongest correlation with brain gray-matter volume (CON1, p = 0.0246; and CON2, p = 0.0334). Notably, only DUF1220 sequences are consistently significant in both disease and nondisease populations. Taken together, these data strongly implicate the loss of DUF1220 copy number in the etiology of 1q21-associated microcephaly and support the view that DUF1220 domains function as general effectors of evolutionary, pathological, and normal variation in brain size., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)