Nocturnal hypoxemic burden and micro- and macrovascular disease in patients with type 2 diabetes.

BACKGROUND: Micro- and macrovascular diseases are common in patients with type 2 diabetes mellitus (T2D) and may be partly caused by nocturnal hypoxemia. The study aimed to characterize the composition of nocturnal hypoxemic burden and to assess its association with micro- and macrovascular disease in patients with T2D. METHODS: This cross-sectional analysis includes overnight oximetry from 1247 patients with T2D enrolled in the DIACORE (DIAbetes COhoRtE) study. Night-time spent below a peripheral oxygen saturation of 90% (T90) as well as T90 associated with non-specific drifts in oxygen saturation (T90non - specific), T90 associated with acute oxygen desaturation (T90desaturation) and desaturation depths were assessed. Binary logistic regression analyses adjusted for known risk factors (age, sex, smoking status, waist-hip ratio, duration of T2D, HbA1c, pulse pressure, low-density lipoprotein, use of statins, and use of renin-angiotensin-aldosterone system inhibitors) were used to assess the associations of such parameters of hypoxemic burden with chronic kidney disease (CKD) as a manifestation of microvascular disease and a composite of cardiovascular diseases (CVD) reflecting macrovascular disease. RESULTS: Patients with long T90 were significantly more often affected by CKD and CVD than patients with a lower hypoxemic burden (CKD 38% vs. 28%, p < 0.001; CVD 30% vs. 21%, p < 0.001). Continuous T90desaturation and desaturation depth were associated with CKD (adjusted OR 1.01 per unit, 95% CI [1.00; 1.01], p = 0.008 and OR 1.30, 95% CI [1.06; 1.61], p = 0.013, respectively) independently of other known risk factors for CKD. For CVD there was a thresholdeffect, and only severly and very severly increased T90non-specific was associated with CVD ([Q3;Q4] versus [Q1;Q2], adjusted OR 1.51, 95% CI [1.12; 2.05], p = 0.008) independently of other known risk factors for CVD. CONCLUSION: While hypoxemic burden due to oxygen desaturations and the magnitude of desaturation depth were significantly associated with CKD, only severe hypoxemic burden due to non-specific drifts was associated with CVD. Specific types of hypoxemic burden may be related to micro- and macrovascular disease.

Trib1 Deficiency Promotes Hyperlipidemia, Inflammation, and Atherosclerosis in LDL Receptor Knockout Mice.

BACKGROUND: Genetic variants at the TRIB1 gene locus are strongly associated with plasma lipid traits and the risk of coronary artery disease in humans. Here, we analyzed the consequences of Trib1 deficiency on lipid metabolism and atherosclerotic lesion formation in atherosclerosis-susceptible Ldlr-/- mice. METHODS: Trib1-/- mice were crossed onto the Ldlr-/- background to generate double-knockout mice (Trib1-/-Ldlr-/-) and fed a semisynthetic, modified AIN76 diet (0.02% cholesterol and 4.3% fat) until 20 weeks of age. RESULTS: Trib1-/-Ldlr-/- mice had profoundly larger (5.8-fold) and more advanced atherosclerotic lesions at the aortic root as compared with Trib1+/+Ldlr-/- controls. Further, we observed significantly elevated plasma total cholesterol and triglyceride levels in Trib1-/-Ldlr-/- mice, resulting from higher VLDL (very-low-density lipoprotein) secretion. Lipidomics analysis revealed that loss of Trib1 altered hepatic lipid composition, including the accumulation of cholesterol and proinflammatory ceramide species, which was accompanied by signs of hepatic inflammation and injury. Concomitantly, we detected higher plasma levels of IL (interleukin)-6 and LCN2 (lipocalin 2), suggesting increased systemic inflammation in Trib1-/-Ldlr-/- mice. Hepatic transcriptome analysis demonstrated significant upregulation of key genes controlling lipid metabolism and inflammation in Trib1-/-Ldlr-/- mice. Further experiments suggested that these effects may be mediated through pathways involving a C/EPB (CCAAT/enhancer binding protein)-PPARγ (peroxisome proliferator-activated receptor γ) axis and JNK (c-Jun N-terminal kinase) signaling. CONCLUSIONS: We provide experimental evidence that Trib1 deficiency promotes atherosclerotic lesion formation in a complex manner that includes the modulation of lipid metabolism and inflammation.

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. MAIN: Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects ("SNP-to-gene" mapping), (ii) genes with kidney phenotypes in mice or human ("gene-to-phenotype"), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function ("GPS tab") to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data ( https://kidneygps.ur.de/gps/ ). CONCLUSION: With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.

Systemic Evidence for Mitochondrial Dysfunction in Age-Related Macular Degeneration as Revealed by mtDNA Copy Number Measurements in Peripheral Blood.

Mitochondrial dysfunction is a common occurrence in the aging process and is observed in diseases such as age-related macular degeneration (AMD). Increased levels of reactive oxygen species lead to damaged mitochondrial DNA (mtDNA), resulting in dysfunctional mitochondria, and, consequently, mtDNA causes further harm in the retinal tissue. However, it is unclear whether the effects are locally restricted to the high-energy-demanding retinal pigment epithelium or are also systematically present. Therefore, we measured mtDNA copy number (mtDNA-CN) in peripheral blood using a qPCR approach with plasmid normalization in elderly participants with and without AMD from the AugUR study (n = 2262). We found significantly lower mtDNA-CN in the blood of participants with early (n = 453) and late (n = 170) AMD compared to AMD-free participants (n = 1630). In regression analyses, we found lower mtDNA-CN to be associated with late AMD when compared with AMD-free participants. Each reduction of mtDNA-CN by one standard deviation increased the risk for late AMD by 24%. This association was most pronounced in geographic atrophy (OR = 1.76, 95% CI 1.19-2.60, p = 0.004), which has limited treatment options. These findings provide new insights into the relationship between mtDNA-CN in blood and AMD, suggesting that it may serve as a more accessible biomarker than mtDNA-CN in the retina.

Diverse molecular causes of unsolved autosomal dominant tubulointerstitial kidney diseases.

Autosomal Dominant Tubulointerstitial Kidney Disease (ADTKD) is caused by mutations in one of at least five genes and leads to kidney failure usually in mid adulthood. Throughout the literature, variable numbers of families have been reported, where no mutation can be found and therefore termed ADTKD-not otherwise specified. Here, we aim to clarify the genetic cause of their diseases in our ADTKD registry. Sequencing for all known ADTKD genes was performed, followed by SNaPshot minisequencing for the dupC (an additional cytosine within a stretch of seven cytosines) mutation of MUC1. A virtual panel containing 560 genes reported in the context of kidney disease (nephrome) and exome sequencing were then analyzed sequentially. Variants were validated and tested for segregation. In 29 of the 45 registry families, mutations in known ADTKD genes were found, mostly in MUC1. Sixteen families could then be termed ADTKD-not otherwise specified, of which nine showed diagnostic variants in the nephrome (four in COL4A5, two in INF2 and one each in COL4A4, PAX2, SALL1 and PKD2). In the other seven families, exome sequencing analysis yielded potential disease associated variants in novel candidate genes for ADTKD; evaluated by database analyses and genome-wide association studies. For the great majority of our ADTKD registry we were able to reach a molecular genetic diagnosis. However, a small number of families are indeed affected by diseases classically described as a glomerular entity. Thus, incomplete clinical phenotyping and atypical clinical presentation may have led to the classification of ADTKD. The identified novel candidate genes by exome sequencing will require further functional validation.

Framework and baseline examination of the German National Cohort (NAKO).

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19-74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2-3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4-5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.

Changes in healthcare seeking and lifestyle in old aged individuals during COVID-19 lockdown in Germany: the population-based AugUR study.

BACKGROUND: Containment measures in the COVID-19 pandemic protected individuals at high risk, particularly individuals at old age, but little is known about how these measures affected health-related behavior of old aged individuals. We aimed to investigate the impact of the spring 2020 lockdown in Germany on healthcare-seeking and health-related lifestyle in the old aged and to identify susceptible subgroups. METHODS: We conducted a follow-up survey among the pre-pandemically well-characterized participants of our AugUR cohort study, residents in/around Regensburg aged 70+ years and relatively mobile. A self-completion questionnaire on current behavior, perceived changes, and SARS-Cov-2 infection was mailed in May 2020, shortly before contact restrictions ended. Pre-pandemic lifestyle and medical conditions were derived from previous study center visits. RESULTS: Among 1850 survey participants (73-98 years; net-response 89%), 74% were at increased risk for severe COVID-19 according to medical conditions; four participants reported SARS-CoV-2 infection (0.2%). Participants reported changes in behavior: 29% refrained from medical appointments, 14% increased TV consumption, 26% reported less physical activity, but no systematic increase of smoking or alcohol consumption. When comparing during- and pre-lockdown reports of lifestyle within participant, we found the same pattern as for the reported perceived changes. Women and the more educated were more susceptible to changes. Worse QOL was perceived by 38%. CONCLUSIONS: Our data suggest that the spring 2020 lockdown did not affect the lifestyle of a majority of the mobile old aged individuals, but the substantial proportions with decreased physical activity and healthcare-seeking are markers of collateral damage.

Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration.

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning-based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1, CFH) from artifacts (near HERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof-of-concept that a GWAS using machine learning-derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.

Frequency of hand eczema in the elderly: Cross-sectional findings from the German AugUR study.

BACKGROUND: Hand eczema (HE) is a chronic inflammatory skin disease caused by a genetic predisposition and environmental exposures. There is a lack of population-based studies on skin diseases in the elderly. OBJECTIVES: Our aim was to estimate the frequency of HE in the elderly to determine its burden of disease in this particular population. METHODS: We analyzed data from the research platform AugUR, a study on chronic diseases in the elderly (n = 1133, ages 70-95 years, mean age 77.6, 45.1% women). Raw frequencies were estimated using self-reports on physician-diagnosed HE from a standardized personal interview. Frequencies were standardized to the Bavarian population weighted by gender and 5-year age-groups. RESULTS: In our sample 2.7% (95% confidence interval [CI] 1.6-4.3) of the paticipants reported to ever have been diagnosed with HE. Among those 57% were male. After standardization, the frequency was estimated at 2.8% (95% CI 1.9-3.9). There were no differences between male and female participants. CONCLUSIONS: Compared to other studies on lifetime frequency of HE, our estimates seem to be remarkably lower. More in-depth studies with validated diagnoses are warranted to precisely estimate the burden of HE in the elderly.

On the differences between mega- and meta-imputation and analysis exemplified on the genetics of age-related macular degeneration.

While current genome-wide association analyses often rely on meta-analysis of study-specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega-imputation and mega-analysis) or study-specifically (meta-imputation and meta-analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age-related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis. From 27,448,454 genetic variants after 1,000-Genomes-based imputation, mega-imputation yielded ~400,000 more variants with high imputation quality (mostly rare variants) compared to meta-imputation. For AMD signal detection (P < 5 × 10-8 ) in mega-imputed data, most loci were detected with mega-analysis without adjusting for study membership (40 loci, including 34 known); we considered these loci genuine, since genetic effects and P-values were comparable across analyses. In meta-imputed data, we found 31 additional signals, mostly near chromosome tails or reference panel gaps, which disappeared after accounting for interaction of whole-genome amplification (WGA) with study membership or after excluding studies with WGA-participants. For signal detection with multistudy IPD, we recommend mega-imputation and mega-analysis, with meta-imputation followed by meta-analysis being a computationally appealing alternative.

Heterogeneity in Obesity: Genetic Basis and Metabolic Consequences.

PURPOSE OF REVIEW: Our review provides a brief summary of the most recent advances towards the identification of the genetic basis of specific aspects of obesity and the quantification of their consequences on health. We also highlight the most promising avenues to be explored in the future. RECENT FINDINGS: While obesity has been demonstrated to lead to adverse cardio-metabolic consequences, the determinants of inter-individual variability remain largely unknown. The elucidation of the molecular underpinnings of this relationship is hampered by the extremely heterogeneous nature of obesity as a human trait. Recent technological advances have facilitated a more in-depth characterization of body composition at large-scale. At the pace of current data acquisition and resolution, it is realistic to improve characterization of obesity and to advise individuals based on detailed body composition combined with tissue-specific molecular signatures. Individualized predictions of health implications would enable more personalized and effective public health interventions.

Response misclassification in studies on bilateral diseases.

Misclassification in binary outcomes can severely bias effect estimates of regression models when the models are naively applied to error-prone data. Here, we discuss response misclassification in studies on the special class of bilateral diseases. Such diseases can affect neither, one, or both entities of a paired organ, for example, the eyes or ears. If measurements are available on both organ entities, disease occurrence in a person is often defined as disease occurrence in at least one entity. In this setting, there are two reasons for response misclassification: (a) ignorance of missing disease assessment in one of the two entities and (b) error-prone disease assessment in the single entities. We investigate the consequences of ignoring both types of response misclassification and present an approach to adjust the bias from misclassification by optimizing an adequate likelihood function. The inherent modelling assumptions and problems in case of entity-specific misclassification are discussed. This work was motivated by studies on age-related macular degeneration (AMD), a disease that can occur separately in each eye of a person. We illustrate and discuss the proposed analysis approach based on real-world data of a study on AMD and simulated data.

A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

PURPOSE: Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. DESIGN: Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. PARTICIPANTS: We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. METHODS: We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. MAIN OUTCOME MEASURES: κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. RESULTS: A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%-92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly. CONCLUSIONS: Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

NFAT5 and SLC4A10 Loci Associate with Plasma Osmolality.

Disorders of water balance, an excess or deficit of total body water relative to body electrolyte content, are common and ascertained by plasma hypo- or hypernatremia, respectively. We performed a two-stage genome-wide association study meta-analysis on plasma sodium concentration in 45,889 individuals of European descent (stage 1 discovery) and 17,637 additional individuals of European descent (stage 2 replication), and a transethnic meta-analysis of replicated single-nucleotide polymorphisms in 79,506 individuals (63,526 individuals of European descent, 8765 individuals of Asian Indian descent, and 7215 individuals of African descent). In stage 1, we identified eight loci associated with plasma sodium concentration at P<5.0 × 10-6 Of these, rs9980 at NFAT5 replicated in stage 2 meta-analysis (P=3.1 × 10-5), with combined stages 1 and 2 genome-wide significance of P=5.6 × 10-10 Transethnic meta-analysis further supported the association at rs9980 (P=5.9 × 10-12). Additionally, rs16846053 at SLC4A10 showed nominally, but not genome-wide, significant association in combined stages 1 and 2 meta-analysis (P=6.7 × 10-8). NFAT5 encodes a ubiquitously expressed transcription factor that coordinates the intracellular response to hypertonic stress but was not previously implicated in the regulation of systemic water balance. SLC4A10 encodes a sodium bicarbonate transporter with a brain-restricted expression pattern, and variant rs16846053 affects a putative intronic NFAT5 DNA binding motif. The lead variants for NFAT5 and SLC4A10 are cis expression quantitative trait loci in tissues of the central nervous system and relevant to transcriptional regulation. Thus, genetic variation in NFAT5 and SLC4A10 expression and function in the central nervous system may affect the regulation of systemic water balance.

Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index.

The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity.

Multi-ethnic fine-mapping of 14 central adiposity loci.

The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ± 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality.

EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data.

UNLABELLED: The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene-strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion. AVAILABILITY: EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IDGenerator: unique identifier generator for epidemiologic or clinical studies.

BACKGROUND: Creating study identifiers and assigning them to study participants is an important feature in epidemiologic studies, ensuring the consistency and privacy of the study data. The numbering system for identifiers needs to be random within certain number constraints, to carry extensions coding for organizational information, or to contain multiple layers of numbers per participant to diversify data access. Available software can generate globally-unique identifiers, but identifier-creating tools meeting the special needs of epidemiological studies are lacking. We have thus set out to develop a software program to generate IDs for epidemiological or clinical studies. RESULTS: Our software IDGenerator creates unique identifiers that not only carry a random identifier for a study participant, but also support the creation of structured IDs, where organizational information is coded into the ID directly. This may include study center (for multicenter-studies), study track (for studies with diversified study programs), or study visit (baseline, follow-up, regularly repeated visits). Our software can be used to add a check digit to the ID to minimize data entry errors. It facilitates the generation of IDs in batches and the creation of layered IDs (personal data ID, study data ID, temporary ID, external data ID) to ensure a high standard of data privacy. The software is supported by a user-friendly graphic interface that enables the generation of IDs in both standard text and barcode 128B format. CONCLUSION: Our software IDGenerator can create identifiers meeting the specific needs for epidemiologic or clinical studies to facilitate study organization and data privacy. IDGenerator is freeware under the GNU General Public License version 3; a Windows port and the source code can be downloaded at the Open Science Framework website: https://osf.io/urs2g/ .

Hundreds of variants clustered in genomic loci and biological pathways affect human height.

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.