cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5124580
title: Supplementary Material for: Should the Negativity for Islet Cell Autoantibodies Be Used in a Prescreening for Genetic Testing in Maturity-Onset Diabetes of the Young? The Case of Autoimmunity-Associated Destruction of Pancreatic β-Cells in a Family of HNF1A-MODY Subjects

description: It was recently suggested that routine islet cell autoantibody testing should be performed to discriminate maturity-onset diabetes of the young (MODY) from type 1 diabetes mellitus (T1DM). This is the first report ever to describe the familial manifestation of T1DM autoimmunity in nonobese HNF1A-MODY subjects and the presence of islet antigen-2 (IA-2) antibodies in MODY subjects. Three nonobese subjects in an age range of 14-35 years were diagnosed with HNF1A-MODY (p. Arg159Gln mutation). All the tested subjects had detectable (but varying) levels of islet cell autoantibodies (i.e., antibodies against glutamate decarboxylase or IA-2) in the absence of other T1DM characteristics. They displayed long-term expression of intermediate fasting C-peptide levels, ketoacidosis was absent even in periods of spontaneous insulin withdrawal, and full dependence on externally administered insulin was not detected in any of them although better glycemic control was achieved when insulin was supplemented. The course of the disease was similar to that of the autoantibody-negative HNF1A-MODY subjects. The case questions the selectivity of autoantibodies as a marker of T1DM or late-onset autoimmune diabetes of adulthood (LADA) over MODY and challenges the use of autoantibodies as a universal negative marker of MODY in an effort to decrease the cost of health care, as it may eventually lead to the wrong diagnosis and thus to the incorrect treatment. Further research should involve examination of the autoantibody titers and prevalence in large and geographically diverse cohorts of MODY subjects selected for genetic testing (regardless of their autoantibody titers) as well as determination of the islet cell autoantibody kinetics in the course of MODY onset and progression.
DOI: 10.6084/m9.figshare.5124571
title: Supplementary Material for: Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?

description: <b><i>Objective:</i></b> To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models. <b><i>Methods:</i></b> To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly. <b><i>Results:</i></b> When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions. <b><i>Conclusion:</i></b> This approach can be used in specific situations but is not appropriate for a primary analysis.
DOI: 10.6084/m9.figshare.5124574
title: Supplementary Material for: Population Genetics of Rare Variants and Complex Diseases

description: <b><i>Objectives:</i></b> Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history. <b><i>Methods:</i></b> Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral. <b><i>Results:</i></b> Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility. <b><i>Conclusions:</i></b> The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.
DOI: 10.6084/m9.figshare.5124574.v1
title: Supplementary Material for: Population Genetics of Rare Variants and Complex Diseases

description: <b><i>Objectives:</i></b> Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history. <b><i>Methods:</i></b> Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral. <b><i>Results:</i></b> Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility. <b><i>Conclusions:</i></b> The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.
DOI: 10.6084/m9.figshare.5124577.v1
title: Supplementary Material for: Vitamin D Treatment and Mortality in Chronic Kidney Disease: A Systematic Review and Meta-Analysis

description: <b><i>Background/Aims:</i></b> Hypovitaminosis D has been associated with an increased cardiovascular mortality in the general population and in patients with chronic kidney disease (CKD). Still, whether prescribing vitamin D reduces the risk of mortality in renal patients remains controversial. <b><i>Methods:</i></b> We searched PubMed, ClinicalTrials.gov and the Cochrane Library for long-term longitudinal studies comparing vitamin D compounds (25-hydroxyvitamin D, 1,25-dihydroxyvitamin D and synthetic derivatives) to placebo or no treatment in renal patients, and which evaluated mortality, to perform a meta-analysis. Data concerning study quality, population and effect size were extracted independently by two investigators using predefined forms. <b><i>Results:</i></b> Fourteen observational studies (194,932 patients) met all eligibility criteria. Most studies were performed in hemodialysis patients and all used calcitriol or synthetic analogues. In a random effects meta-analysis, receiving any vitamin D therapy significantly reduced the risk of all-cause mortality (relative risk 0.73, 95% CI 0.65-0.82). The relative risk of death was 0.72 (95% CI 0.65-0.80) after 3 years of therapy and 0.67 (95% CI 0.45-0.98) after 5 years. In meta-regression, the risk reduction was shown to be greater in patients with higher parathyroid hormone serum levels (p = 0.01). The risk of cardiovascular mortality was also significantly reduced in patients receiving any vitamin D derivative (relative risk 0.63, 95% CI 0.44-0.92). <b><i>Conclusion:</i></b> Therapies with 1,25-dihydroxyvitamin D and analogues are associated with reduced mortality in CKD patients, and particularly in those suffering from secondary hyperparathyroidism. These results, based on observational evidence, are supportive of prescribing vitamin D therapies to CKD patients, while respecting good practice guidelines.
DOI: 10.6084/m9.figshare.5124568
title: Supplementary Material for: Single-Variant and Multi-Variant Trend Tests for Genetic Association with Next-Generation Sequencing That Are Robust to Sequencing Error

description: As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or <i>LTT</i><sub>ae,NGS</sub>. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the <i>LTT</i><sub>ae,NGS</sub> maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of <i>LTT</i><sub>ae,NGS</sub> shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our <i>LTT</i><sub>ae,NGS</sub> addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.
DOI: 10.6084/m9.figshare.5124580.v1
title: Supplementary Material for: Should the Negativity for Islet Cell Autoantibodies Be Used in a Prescreening for Genetic Testing in Maturity-Onset Diabetes of the Young? The Case of Autoimmunity-Associated Destruction of Pancreatic β-Cells in a Family of HNF1A-MODY Subjects

description: It was recently suggested that routine islet cell autoantibody testing should be performed to discriminate maturity-onset diabetes of the young (MODY) from type 1 diabetes mellitus (T1DM). This is the first report ever to describe the familial manifestation of T1DM autoimmunity in nonobese HNF1A-MODY subjects and the presence of islet antigen-2 (IA-2) antibodies in MODY subjects. Three nonobese subjects in an age range of 14-35 years were diagnosed with HNF1A-MODY (p. Arg159Gln mutation). All the tested subjects had detectable (but varying) levels of islet cell autoantibodies (i.e., antibodies against glutamate decarboxylase or IA-2) in the absence of other T1DM characteristics. They displayed long-term expression of intermediate fasting C-peptide levels, ketoacidosis was absent even in periods of spontaneous insulin withdrawal, and full dependence on externally administered insulin was not detected in any of them although better glycemic control was achieved when insulin was supplemented. The course of the disease was similar to that of the autoantibody-negative HNF1A-MODY subjects. The case questions the selectivity of autoantibodies as a marker of T1DM or late-onset autoimmune diabetes of adulthood (LADA) over MODY and challenges the use of autoantibodies as a universal negative marker of MODY in an effort to decrease the cost of health care, as it may eventually lead to the wrong diagnosis and thus to the incorrect treatment. Further research should involve examination of the autoantibody titers and prevalence in large and geographically diverse cohorts of MODY subjects selected for genetic testing (regardless of their autoantibody titers) as well as determination of the islet cell autoantibody kinetics in the course of MODY onset and progression.
DOI: 10.6084/m9.figshare.5124559
title: Supplementary Material for: Prenatal Diagnosis of Congenital Epulis by 2D/3D Ultrasound and Magnetic Resonance

description: Congenital epulis is a rare benign oral cavity tumor that usually arises from the maxillary alveolar mucosa. It is also known as congenital gingival granular cell tumor. Prenatal diagnosis is uncommon and mostly confined to the third trimester. We report a case of congenital epulis, which was referred to our department at 35 weeks of gestation. Both images from our prenatal 2D/3D ultrasound (including Doppler technique) and magnetic resonance examination are presented. A baby girl weighing 2,800 g was delivered spontaneously at 36 weeks and 1 day. The newborn had to be intubated immediately after delivery. A simple excision of the mass was performed on the first day of neonatal life after clinical examination by our pediatric stomatologists confirmed the presence of a tumor resembling epulis. The correctness of this diagnosis was subsequently confirmed by histogenesis. Photographs from the operating room show the postnatal appearance of the tumor. The baby was discharged at the age of 19 days and has remained well at follow-up controls.
DOI: 10.6084/m9.figshare.5124562
title: Supplementary Material for: Urine Catalytic Iron and Neutrophil Gelatinase-Associated Lipocalin as Companion Early Markers of Acute Kidney Injury after Cardiac Surgery: A Prospective Pilot Study

description: <b><i>Background:</i></b> Open heart surgery with cardiopulmonary bypass is recognized as a common cause of acute kidney injury (AKI). The conventional biomarker creatinine is not sensitive enough to detect AKI until a significant decline in renal filtration has occurred. Urine neutrophil gelatinase-associated lipocalin (NGAL), part of an acute response to the release of tissue iron from cells, is an early biomarker and a predictor of AKI in a variety of clinical settings. We sought to evaluate the relationship between urine catalytic iron (unbound iron) and NGAL over the course of AKI due to cardiac surgery. <b><i>Methods:</i></b> Fourteen patients who underwent open heart surgery had the following measured: serum creatinine (0, 12, 24, 48 and 72 h postoperatively), urine NGAL and urine catalytic iron (0, 8, 24 and 48 h postoperatively). Urine NGAL and urine catalytic iron were quantified by immunoassay and bleomycin-detectable iron assay, respectively. AKI was defined by the Acute Kidney Injury Network (AKIN) criteria. <b><i>Results:</i></b> Urine catalytic iron increased significantly (p &lt; 0.05) within 8 h and peaked at 24 h postoperatively in patients who developed AKI (n = 8, baseline 101.96 ± 177.48, peak 226.35 ± 238.23 nmol/l, p = 0.006), but not in non-AKI patients (n = 6, baseline 131.08 ± 116.21, peak 163.99 ± 109.62 nmol/l, p = 0.380). Urine NGAL levels also peaked at 24 h with significant increase observed only in AKI patients: AKI – baseline 34.88 ± 26.47, peak 65.50 ± 27.03 ng/ml, p = 0.043; non-AKI – baseline 59.33 ± 31.72, peak 71.00 ± 31.76 ng/ml, p = 0.100. The correlation between baseline levels of urine catalytic iron and NGAL and peak levels of urine catalytic iron and NGAL was r = 0.86, p &lt; 0.0001. <b><i>Conclusion:</i></b> Urine catalytic iron appears to rise and fall in concert with NGAL in patients undergoing cardiac surgery and may be indicative of early AKI. Future research into the role that catalytic iron plays in acute organ injury syndromes and its potential diagnostic and therapeutic implications is warranted.
DOI: 10.6084/m9.figshare.5124565
title: Supplementary Material for: Preclinical Cardiorenal Interrelationships in Essential Hypertension

description: A diseased heart causes numerous adverse effects on kidney function, and vice versa renal disease can significantly impair cardiac function. Beyond these heart-kidney interrelationships at the clinical level, a reciprocal association has been suggested to exist even in the early stages of those organs' dysfunction. The aim of the present review is to provide evidence of the presence of a preclinical cardiorenal syndrome in the particular setting of essential hypertension, focusing on the subsequent hypertensive sequelae on heart and kidneys. In particular, a plethora of studies have demonstrated not only the predictive role of kidney damage, as expressed by either decreased glomerular filtration or increased urine albumin excretion, for adverse left ventricular functional and structural adaptations but also preclinical heart disease, i.e. left ventricular hypertrophy that is associated with deterioration of renal function. Notably, these reciprocal interactions seem to exist even at the level of microcirculation, since both coronary flow reserve and renal hemodynamics are strongly related with clinical and preclinical renal and cardiac damage, respectively. In this preclinical setting, common pathophysiological denominators, including the increased hemodynamic load, sympathetic and renin-angiotensin system overactivity, increased subclinical inflammatory reaction, and endothelial dysfunction, account not only for the reported associations between overt cardiac and renal damage but also for the parallel changes that occur in coronary and renal microcirculation.
DOI: 10.6084/m9.figshare.5124571.v1
title: Supplementary Material for: Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?

description: <b><i>Objective:</i></b> To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models. <b><i>Methods:</i></b> To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly. <b><i>Results:</i></b> When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions. <b><i>Conclusion:</i></b> This approach can be used in specific situations but is not appropriate for a primary analysis.
DOI: 10.6084/m9.figshare.5124556
title: Supplementary Material for: Could Inbred Cases Identified in GWAS Data Succeed in Detecting Rare Recessive Variants Where Affected Sib-Pairs Have Failed?

description: To detect fully penetrant rare recessive variants that could constitute Mendelian subentities of complex diseases, we propose a novel strategy, the HBD-GWAS strategy, which can be applied to genome-wide association study (GWAS) data. This strategy first involves the identification of inbred individuals among cases using the genome-wide SNP data and then focuses on these inbred affected individuals and searches for genomic regions of shared homozygosity by descent that could harbor rare recessive disease-causing variants. In this second step, analogous to homozygosity mapping, a heterogeneity lod-score, HFLOD, is computed to quantify the evidence of linkage provided by the data. In this paper, we evaluate this strategy theoretically under different scenarios and compare its performances with those of linkage analysis using affected sib-pair (ASP) data. If cases affected by these Mendelian subentities are not enriched in the sample of cases, the HBD-GWAS strategy has almost no power to detect them, unless they explain an important part of the disease prevalence. The HBD-GWAS strategy outperforms the ASP linkage strategy only in a very limited number of situations where there exists a strong allelic heterogeneity. When several rare recessive variants within the same gene are involved, the ASP design indeed often fails to detect the gene, whereas, by focusing on inbred individuals using the HBD-GWAS strategy, the gene might be detected provided very large samples of cases are available.