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Abstract Speech-in-noise (SIN) perception is a primary complaint of individuals with audiometric hearing loss. SIN performance varies drastically, even among individuals with normal hearing. The present genome-wide association study (GWAS) investigated the genetic basis of SIN deficits in individuals with self-reported normal hearing in quiet situations. GWAS was performed on 279,911 individuals from the UB Biobank cohort, with 58,847 reporting SIN deficits despite reporting normal hearing in quiet. GWAS identified 996 single nucleotide polymorphisms (SNPs), achieving significance (p

Objective: The present study investigated the epidemic of tinnitus in college-aged young adults. Our first objective was to identify health conditions associated with tinnitus in young adults. The second objective was to evaluate the predictive utility of some known risk factors. Study design: A cross-sectional design was used to investigate the prevalence and risk factors for tinnitus. Setting: A questionnaire was distributed, reaching out to a large college-aged population. A total of 2258 young adults aged 18–30 years were recruited from April 2021 to February 2022. Interventions: A questionnaire was administered to investigate the epidemiology of tinnitus in a population of college-aged young adults. Results: About 17.7% of young adults reported bothersome tinnitus perception lasting for ≥5 min in the last 12 months. The prevalence of chronic tinnitus (bothersome tinnitus for ≥1 year) and acute tinnitus (bothersome tinnitus for

Molybdenum-99 (99Mo) is used for preparing Technetium-99 m (99mTc), which is the most widely used isotope in nuclear medicine. In this work, a study for 99Mo production based on a high-power electron accelerator has been performed as an alternative approach to produce 99mTc. In this study, Monte Carlo MCNPX2.6 code has been used to examine a novel idea of simultaneous hybrid production of 99Mo via both photoneutron and neutron capture reactions using an electron accelerator in heavy water tank. It is expected that this conceptual design including an arrangement of metallic plates of 100Mo and 98Mo produces total activity of 97.5 Ci at the end of 20-h continuous e-beam irradiation (30 MeV, 10 mA).

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Abstract Tinnitus, a phantom perception of sound in the absence of any external sound source, is a prevalent health condition often accompanied by psychiatric comorbidities. Recent genome-wide association studies (GWAS) highlighted a polygenic nature of tinnitus susceptibility. A shared genetic component between tinnitus and psychiatric conditions remains elusive. Here we present a GWAS using the UK Biobank to investigate the genetic processes linked to tinnitus and tinnitus-related distress, followed by gene-set enrichment analyses. The UK Biobank sample comprised 132,438 individuals with tinnitus and genotype data. Among the study sample, 38,525 individuals reported tinnitus, and 26,889 participants mentioned they experienced tinnitus-related distress in daily living. The genome-wide association analyses were conducted on tinnitus and tinnitus-related distress. We conducted enrichment analyses using FUMA to further understand the genetic processes linked to tinnitus and tinnitus-related distress. A genome-wide significant locus (lead SNP: rs71595470) for tinnitus was obtained in the vicinity of GPM6A. Nineteen independent loci reached suggestive association with tinnitus. Fifteen independent loci reached suggestive association with tinnitus-related distress. The enrichment analysis revealed a shared genetic component between tinnitus and psychiatric traits, such as bipolar disorder, feeling worried, cognitive ability, fast beta electroencephalogram, and sensation seeking. Metabolic, cardiovascular, hematological, and pharmacological gene sets revealed a significant association with tinnitus. Anxiety and stress-related gene sets revealed a significant association with tinnitus-related distress. The GWAS signals for tinnitus were enriched in the hippocampus and cortex, and for tinnitus-related distress were enriched in the brain and spinal cord. This study provides novel insights into genetic processes associated with tinnitus and tinnitus-related distress and demonstrates a shared genetic component underlying tinnitus and psychiatric conditions. Further collaborative attempts are necessary to identify genetic components underlying the phenotypic heterogeneity in tinnitus and provide biological insight into the etiology.

Abstract Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

Abstract Background Diabetic kidney disease (DKD) affects about 40% of patients with diabetes. It is incurable and usually leads to end-stage renal disease (ESRD). The pathogenesis of DKD is still not fully understood, and the genetics of DKD have not yet been extensively studied. In this study, we investigate the genetic basis of DKD in type 2 diabetes (T2D) to provide more insights into the pathogenesis of the disease. Results Using the data provided by the UK Biobank (UKBB), we performed a DKD genome-wide association study (GWAS) in 13,123 individuals with T2D as well as two creatinine estimated glomerular filtration rate (eGFR) GWA studies: one in 26,786 individuals with T2D and the other in 339,080 non-diabetic individuals. We also conducted a DKD GWAS meta-analysis combining our results with those published by the surrogate markers for micro- and macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) consortium. We confirm two loci previously reported to be associated with chronic kidney disease (CKD) and eGFR in T2D. The UMOD-PDILT locus is associated with DKD (P = 1.17E−09) as well as creatinine eGFR in both people with T2D (P = 1.31E−15) and people without diabetes (P = 3.95E−73). The PRKAG2 locus is associated with creatinine eGFR in people with (P = 2.78E−10) and without (P = 5.65E−72) T2D. Our meta-analysis reveals a novel association between DKD and variant rs72763500 (chr1:236116561) which is a splicing quantitative trait locus (sQTL) for nidogen-1 (NID1) gene. Conclusion Our data confirm two loci previously reported in association with CKD and creatinine eGFR in T2D. It also suggests that NID1, a major component of the renal tubular basement membrane, could play a role in DKD development in T2D. While our NID1 finding remains to be replicated, it is a step toward a more comprehensive understanding of DKD pathogenesis.

Single-cell RNA sequencing and systems biology are used to profile the human vascular cell populations in calcified atherosclerotic core plaques from carotid endarterectomy samples, showing an anatomic distinction between gene expression of inflammatory versus matrix-secreting factors.

Abstract Parkinson’s disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson’s Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson’s Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66–0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Abstract We developed a smartphone application, MyGeneRank, to conduct a prospective observational cohort study (NCT03277365) involving the automated generation, communication, and electronic capture of response to a polygenic risk score (PRS) for coronary artery disease (CAD). Adults with a smartphone and an existing 23andMe genetic profiling self-referred to the study. We evaluated self-reported actions taken in response to personal CAD PRS information, with special interest in the initiation of lipid-lowering therapy. 19% (721/3,800) of participants provided complete responses for baseline and follow-up use of lipid-lowering therapy. 20% (n = 19/95) of high CAD PRS vs 7.9% (n = 8/101) of low CAD PRS participants initiated lipid-lowering therapy at follow-up (p-value = 0.002). Both the initiation of statin and non-statin lipid-lowering therapy was associated with degree of CAD PRS: 15.2% (n = 14/92) vs 6.0% (n = 6/100) for statins (p-value = 0.018) and 6.8% (n = 8/118) vs 1.6% (n = 2/123) for non-statins (p-value = 0.022) in high vs low CAD PRS, respectively. High CAD PRS was also associated with earlier initiation of lipid lowering therapy (average age of 52 vs 65 years in high vs low CAD PRS respectively, p-value = 0.007). Overall, degree of CAD PRS was associated with use of any lipid-lowering therapy at follow-up: 42.4% (n = 56/132) vs 28.5% (n = 37/130) (p-value = 0.009). We find that digital communication of personal CAD PRS information is associated with increased and earlier lipid-lowering initiation in individuals of high CAD PRS. Loss to follow-up is the primary limitation of this study. Alternative communication routes, and long-term studies with EHR-based outcomes are needed to understand the generalizability and durability of this finding.

Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self-report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance runners. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance.

Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here, we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least fourfold faster inference run time relative to standard imputation tools.

Abstract N-Acetylneuraminic acid is the most abundant sialic acid (SA) in humans and is expressed as the terminal sugar on intestinal mucus glycans. Several pathogenic bacteria harvest and display host SA on their own surfaces to evade Siglec-mediated host immunity. While previous studies have identified bacterial enzymes associated with SA catabolism, no reported methods permit the selective labeling, tracking, and quantitation of SA-presenting microbes within complex multi-microbial systems. We combined metabolic labeling, click chemistry, 16S rRNA gene, and whole-genome sequencing to track and identify SA-presenting microbes from a cultured human fecal microbiome. We isolated a new strain of Escherichia coli that incorporates SA onto its own surface and encodes for the nanT, neuA, and neuS genes necessary for harvesting and presenting SA. Our method is applicable to the identification of SA-presenting bacteria from human, animal, and environmental microbiomes, as well as providing an entry point for the investigation of surface-expressed SA-associated structures.

Abstract Background Next Generation Sequencing (NGS) is the fundament of various studies, providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors in single nucleotide variant calls, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq, and (iii) Complete Genomics and processed with the respective bioinformatic pipeline. We also repeated variant calling for the Illumina cohorts with GATK, which allowed us to investigate the effect of the bioinformatics analysis strategy separately from the sequencing platform’s impact. Results The number of detected variants/variant classes per individual was highly dependent on the experimental setup. We observed a statistically significant overrepresentation of variants uniquely called by a single setup, indicating potential systematic biases. Insertion/deletion polymorphisms (indels) were associated with decreased concordance compared to single nucleotide polymorphisms (SNPs). The discrepancies in indel absolute numbers were particularly prominent in introns, Alu elements, simple repeats, and regions with medium GC content. Notably, reprocessing sequencing data following the best practice recommendations of GATK considerably improved concordance between the respective setups. Conclusion We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Furthermore, our results demonstrate the benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.

Mislabeling of cases as well as controls in case–control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow for consistent and robust treatment of labeled data in the situations where both, the case and the control groups, contain a non-negligible proportion of mislabeled data instances. This is an especially prominent issue in studies regarding late-onset conditions, where individuals who may convert to cases may populate the control group, and for screening studies that often have high false-positive/-negative rates. To address this problem, we propose a method for a simultaneous robust inference of Lasso reduced discriminative models and of latent group-specific mislabeling risks, not requiring any exactly labeled data. We apply it to a standard breast cancer imaging dataset and infer the mislabeling probabilities (being rates of false-negative and false-positive core-needle biopsies) together with a small set of simple diagnostic rules, outperforming the state-of-the-art BI-RADS diagnostics on these data. The inferred mislabeling rates for breast cancer biopsies agree with the published purely empirical studies. Applying the method to human genomic data from a healthy-ageing cohort reveals a previously unreported compact combination of single-nucleotide polymorphisms that are strongly associated with a healthy-ageing phenotype for Caucasians. It determines that 7.5% of Caucasians in the 1000 Genomes dataset (selected as a control group) carry a pattern characteristic of healthy ageing.

Abstract Background Whole-exome sequencing (WES) has become an efficient diagnostic test for patients with likely monogenic conditions such as rare idiopathic diseases or sudden unexplained death. Yet, many cases remain undiagnosed. Here, we report the added diagnostic yield achieved for 101 WES cases re-analyzed 1 to 7 years after initial analysis. Methods Of the 101 WES cases, 51 were rare idiopathic disease cases and 50 were postmortem “molecular autopsy” cases of early sudden unexplained death. Variants considered for reporting were prioritized and classified into three groups: (1) diagnostic variants, pathogenic and likely pathogenic variants in genes known to cause the phenotype of interest; (2) possibly diagnostic variants, possibly pathogenic variants in genes known to cause the phenotype of interest or pathogenic variants in genes possibly causing the phenotype of interest; and (3) variants of uncertain diagnostic significance, potentially deleterious variants in genes possibly causing the phenotype of interest. Results Initial analysis revealed diagnostic variants in 13 rare disease cases (25.4%) and 5 sudden death cases (10%). Re-analysis resulted in the identification of additional diagnostic variants in 3 rare disease cases (5.9%) and 1 sudden unexplained death case (2%), which increased our molecular diagnostic yield to 31.4% and 12%, respectively. Conclusions The basis of new findings ranged from improvement in variant classification tools, updated genetic databases, and updated clinical phenotypes. Our findings highlight the potential for re-analysis to reveal diagnostic variants in cases that remain undiagnosed after initial WES.

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.

Abstract Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

Abstract Background Whole genome and exome sequencing usually include reads containing mitochondrial DNA (mtDNA). Yet, state-of-the-art pipelines and services for human nuclear genome variant calling and annotation do not handle mitochondrial genome data appropriately. As a consequence, any researcher desiring to add mtDNA variant analysis to their investigations is forced to explore the literature for mtDNA pipelines, evaluate them, and implement their own instance of the desired tool. This task is far from trivial, and can be prohibitive for non-bioinformaticians. Results We have developed SG-ADVISER mtDNA, a web server to facilitate the analysis and interpretation of mtDNA genomic data coming from next generation sequencing (NGS) experiments. The server was built in the context of our SG-ADVISER framework and on top of the MtoolBox platform (Calabrese et al., Bioinformatics 30(21):3115–3117, 2014), and includes most of its functionalities (i.e., assembly of mitochondrial genomes, heteroplasmic fractions, haplogroup assignment, functional and prioritization analysis of mitochondrial variants) as well as a back-end and a front-end interface. The server has been tested with unpublished data from 200 individuals of a healthy aging cohort (Erikson et al., Cell 165(4):1002–1011, 2016) and their data is made publicly available here along with a preliminary analysis of the variants. We observed that individuals over ~90 years old carried low levels of heteroplasmic variants in their genomes. Conclusions SG-ADVISER mtDNA is a fast and functional tool that allows for variant calling and annotation of human mtDNA data coming from NGS experiments. The server was built with simplicity in mind, and builds on our own experience in interpreting mtDNA variants in the context of sudden death and rare diseases. Our objective is to provide an interface for non-bioinformaticians aiming to acquire (or contrast) mtDNA annotations via MToolBox. SG-ADVISER web server is freely available to all users at https://genomics.scripps.edu/mtdna .

Rheumatoid arthritis patients respond differently to anti-TNF treatment. Using community-based challenge, the authors show that currently available data does not reveal meaningful genetic predictors of response to anti-TNF therapy, thus confirming clinical observations.

Essential tremor (ET) and Parkinson’s disease (PD) are among the most common adult-onset tremor disorders. Clinical and pathological studies suggest that misdiagnosis of PD for ET, and vice versa, occur in anywhere from 15% to 35% of cases. Complex diagnostic procedures, such as dopamine transporter imaging, can be powerful diagnostic aids but are lengthy and expensive procedures that are not widely available. Preliminary studies suggest that monitoring of tremor characteristics with consumer grade accelerometer devices could be a more accessible approach to the discrimination of PD from ET, but these studies have been performed in well-controlled clinical settings requiring multiple maneuvers and oversight from clinical or research staff, and thus may not be representative of at-home monitoring in the community setting. Therefore, we set out to determine whether discrimination of PD vs. ET diagnosis could be achieved by monitoring research subject movements at home using consumer grade devices, and whether discrimination could be improved with the addition of genetic profiling of the type that is readily available through direct-to-consumer genetic testing services. Forty subjects with PD and 27 patients with ET were genetically profiled and had their movements characterized three-times a day for two weeks through a simple procedure meant to induce rest tremors. We found that tremor characteristics could be used to predict diagnosis status (sensitivity = 76%, specificity = 65%, area under the curve (AUC) = 0.75), but that the addition of genetic risk information, via a PD polygenic risk score, did not improve discriminatory power (sensitivity = 80%, specificity = 65%, AUC = 0.73).

Circulating tumor DNA (ctDNA) has shown great promise as a biomarker for early detection of cancer. However, due to the low abundance of ctDNA, especially at early stages, it is hard to detect at high accuracies while keeping sequencing costs low. Here we present a pilot stage study to detect large scale somatic copy numbers variations (CNVs), which contribute more molecules to ctDNA signal compared to point mutations, via cell free DNA sequencing. We show that it is possible to detect somatic CNVs in early stage colorectal cancer (CRC) patients and subsequently discriminate them from normal patients. With 25 normal and 24 CRC samples, we achieve 100% specificity (lower bound confidence interval: 86%) and ~79% sensitivity (95% confidence interval: 63% - 95%,), though the performance should be considered with caution given the limited sample size. We report a lack of concordance between the CNVs detected via cfDNA sequencing and CNVs identified in parent tissue samples. However, recent findings suggest that a lack of concordance is expected for CNVs in CRC because of their sub-clonal nature. Finally, the CNVs we detect very likely contribute to cancer progression as they lie in functionally important regions, and have been shown to be associated with CRC specifically. This study paves the path for a larger scale exploration of the potential of CNV detection for both diagnoses and prognoses of cancer.

The Scripps molecular autopsy study seeks to incorporate genetic testing into the postmortem examination of cases of sudden death in the young (

Current high-sensitivity cancer screening methods, largely utilizing correlative biomarkers, suffer from false positive rates that lead to unnecessary medical procedures and debatable public health benefit overall. Detection of circulating tumor DNA (ctDNA), a causal biomarker, has the potential to revolutionize cancer screening. Thus far, the majority of ctDNA studies have focused on detection of tumor-specific point mutations after cancer diagnosis for the purpose of post-treatment surveillance. However, ctDNA point mutation detection methods developed to date likely lack either the scope or analytical sensitivity necessary to be useful for cancer screening, due to the low (

Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease--without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER.

Allele-specific DNA methylation (ASM) is well studied in imprinted domains, but this type of epigenetic asymmetry is actually found more commonly at non-imprinted loci, where the ASM is dictated not by parent-of-origin but instead by the local haplotype. We identified loci with strong ASM in human tissues from methylation-sensitive SNP array data. Two index regions (bisulfite PCR amplicons), one between the C3orf27 and RPN1 genes in chromosome band 3q21 and the other near the VTRNA2-1 vault RNA in band 5q31, proved to be new examples of imprinted DMRs (maternal alleles methylated) while a third, between STEAP3 and C2orf76 in chromosome band 2q14, showed non-imprinted haplotype-dependent ASM. Using long-read bisulfite sequencing (bis-seq) in 8 human tissues we found that in all 3 domains the ASM is restricted to single differentially methylated regions (DMRs), each less than 2kb. The ASM in the C3orf27-RPN1 intergenic region was placenta-specific and associated with allele-specific expression of a long non-coding RNA. Strikingly, the discrete DMRs in all 3 regions overlap with binding sites for the insulator protein CTCF, which we found selectively bound to the unmethylated allele of the STEAP3-C2orf76 DMR. Methylation mapping in two additional genes with non-imprinted haplotype-dependent ASM, ELK3 and CYP2A7, showed that the CYP2A7 DMR also overlaps a CTCF site. Thus, two features of imprinted domains, highly localized DMRs and allele-specific insulator occupancy by CTCF, can also be found in chromosomal domains with non-imprinted ASM. Arguing for biological importance, our analysis of published whole genome bis-seq data from hES cells revealed multiple genome-wide association study (GWAS) peaks near CTCF binding sites with ASM.

Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 1-FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn's disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci.

Nature Communications 7: Article number: 12460 (2016); Published: 23 August 2016; Updated: 10 October 2016. The HTML version of this Article incorrectly duplicated the authors S. Louis Bridges, Lindsey Criswell, Larry Moreland, Lars Klareskog, Saedis Saevarsdottir, Leonid Padyukov, Peter K. Gregersen, Stephen Friend, Robert Plenge, Gustavo Stolovitzky, Baldo Oliva, Yuanfang Guan and Lara M.

The primary abnormality in Down syndrome (DS), trisomy 21, is well known; but how this chromosomal gain produces the complex DS phenotype, including immune system defects, is not well understood. We profiled DNA methylation in total peripheral blood leukocytes (PBL) and T-lymphocytes from adults with DS and normal controls and found gene-specific abnormalities of CpG methylation in DS, with many of the differentially methylated genes having known or predicted roles in lymphocyte development and function. Validation of the microarray data by bisulfite sequencing and methylation-sensitive Pyrosequencing (MS-Pyroseq) confirmed strong differences in methylation (p

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.