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Summary: Background: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study. Methods: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p

Phacomatosis Pigmentovascularis (PPV) comprises a family of rare conditions that feature vascular abnormalities and melanocytic lesions that can be solely cutaneous or multisystem in nature. Recently published work has demonstrated that both vascular and melanocytic abnormalities in PPV of the cesioflammea and cesiomarmorata subtypes can result from identical somatic mosaic activating mutations in the genes GNAQ and GNA11. Here we present three new cases of PPV with features of the cesioflammea and/or cesiomarmorata subtypes and mosaic mutations in GNAQ or GNA11. In order to better understand the risk of potentially occult complications faced by such patients we additionally reviewed 176 cases published in the literature. We report the frequency of clinical findings, their patterns of co-occurrence as well as published recommendations for surveillance after diagnosis. Features assessed include: capillary malformation; dermal and ocular melanocytosis; glaucoma; limb asymmetry; venous malformations; and central nervous system (CNS) anomalies such as ventriculomegaly and calcifications. We found that ocular findings are common in patients with phacomatosis cesioflammea and cesiomarmorata. Facial vascular involvement correlates with a higher risk of seizures (p=0.0066). Our genetic results confirm the role of mosaic somatic mutations in GNAQ and GNA11 in phacomatosis cesioflammea and cesiomarmorata. Their clinical and molecular findings place these conditions on a clinical spectrum encompassing other GNAQ and GNA11 related disorders and inform recommendations for their management.

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure., The genetic and pathogenetic basis of heart failure is incompletely understood. Here, the authors present a high-fidelity tissue collection from rapidly preserved failing and non-failing control hearts which are used for eQTL mapping and network analysis, resulting in the prioritization of PPP1R3A as a heart failure gene.

Insertions and deletions (indels) make a critical contribution to human genetic variation. While indel calling has improved significantly, it lags dramatically in performance relative to single-nucleotide variant calling, something of particular concern for clinical genomics where larger scale disruption of the open reading frame can commonly cause disease. Here, we present a machine learning-based approach to the detection of indel breakpoints called Scotch. This novel approach improves sensitivity to larger variants dramatically by leveraging sequencing metrics and signatures of poor read alignment. We also introduce a meta-analytic indel caller, called Metal, that performs a “smart intersection” of Scotch and currently available tools to be maximally sensitive to large variants. We use new benchmark datasets and Sanger sequencing to compare Scotch and Metal to current gold standard indel callers, achieving unprecedented levels of precision and recall. We demonstrate the impact of these improvements by applying this tool to a cohort of patients with undiagnosed disease, generating plausible novel candidates in 21 out of 26 undiagnosed cases. We highlight the diagnosis of one patient with a 498-bp deletion in HNRNPA1 missed by traditional indel-detection tools.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple’s ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study’s launch and the time of the data freeze for this data release (March 10 2015–October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices., Design Type(s)observation design · source-based data analysis objective · data collection and processing objectiveMeasurement Type(s)physical activity · sleepTechnology Type(s)crowd-sourced data generationFactor Type(s)sex · height · weight · age · smoking status measurement · employment statusSample Characteristic(s)Homo sapiens · United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)

Background We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. Results This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. Conclusion BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general Electronic supplementary material The online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.

Background: Smartphone applications may enable interventions to increase physical activity but this has limited evidence in randomized trials. Objectives: The MyHeart Counts Cardiovascular Health study (MHC) is a smartphone-based longitudinal research study aimed at elucidating the determinants of cardiovascular health. Among MHC participants, we performed a randomized controlled trial to investigate the response to four different physical activity coaching interventions on the primary outcome of change in daily step count. Secondary outcomes included life satisfaction, 6-minute walk distance, and sleep quality. Methods: The trial was completed entirely using personal smartphones: participants were digitally consented, the interventions were delivered via the device, and measurements of the primary and secondary outcomes were collected from smartphone sensors. Participants were enrolled from December 12, 2016 to June 6, 2018 and followed for up to 5 weeks. Participants completed the trial through the MyHeart Counts phone app in a free-living setting. All adults over 18 years of age with access to a smartphone (Apple iPhone, version 5S or later) were eligible to participate. After one week of baseline measurements, participants (n=2783) were randomized to a sequence of four, week-long interventions delivered in random order. Interventions consisted of either daily prompts to complete 10,000 steps (completed by n=853); hourly prompts to stand following a full hour of sitting (completed by n=879); instructions to read the guidelines from the American Heart Association website (completed by n=868); and e-coaching based upon the individual's personal activity patterns from the baseline week of data collection (completed by n=896). Results: 2783 participants consented to enroll in the coaching study, of whom 1075 completed the baseline week of data collection and at least one of the four interventions. 493 individuals completed the full set of assigned interventions. All four interventions were found effective at modestly increasing daily step count by a mean of 266 ±75 steps from a baseline mean of 2958±69 steps (p=0.003). Intervention-specific step increases were: 319±74 steps (p

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.

The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.

BackgroundHand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.AimsIn this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.Methods and ResultsIn our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide significance (P−8). Of these, 64 were associated (PFTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most significant genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated a significant enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents’ age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.ConclusionsIn our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.

PurposeCurrent clinical genomics assays primarily utilize short-read sequencing (SRS), but SRS has limited ability to evaluate repetitive regions and structural variants. Long-read sequencing (LRS) has complementary strengths, and we aimed to determine whether LRS could offer a means to identify overlooked genetic variation in patients undiagnosed by SRS.MethodsWe performed low-coverage genome LRS to identify structural variants in a patient who presented with multiple neoplasia and cardiac myxomata, in whom the results of targeted clinical testing and genome SRS were negative.ResultsThis LRS approach yielded 6,971 deletions and 6,821 insertions 50 bp. Filtering for variants that are absent in an unrelated control and overlap a disease gene coding exon identified three deletions and three insertions. One of these, a heterozygous 2,184 bp deletion, overlaps the first coding exon of PRKAR1A, which is implicated in autosomal dominant Carney complex. RNA sequencing demonstrated decreased PRKAR1A expression. The deletion was classified as pathogenic based on guidelines for interpretation of sequence variants.ConclusionThis first successful application of genome LRS to identify a pathogenic variant in a patient suggests that LRS has significant potential for the identification of disease-causing structural variation. Larger studies will ultimately be required to evaluate the potential clinical utility of LRS.

Next-generation sequencing is making it critical to robustly and rapidly handle genomic ranges within standard pipelines. Standard use-cases include annotating sequence ranges with gene or other genomic annotation, merging multiple experiments together and subsequently quantifying and visualizing the overlap. The most widely-used tools for these tasks work at the command-line (e.g. BEDTools) and the small number of available R packages are either slow or have distinct semantics and features from command-line interfaces.To provide a robust R-based interface to standard command-line tools for genomic coordinate manipulation, we created bedr. This open-source R package can use either BEDTools or BEDOPS as a back-end and performs data-manipulation extremely quickly, creating R data structures that can be readily interfaced with existing computational pipelines. It includes data-visualization capabilities and a number of data-access functions that interface with standard databases like UCSC and COSMIC.bedr package provides an open source solution to enable genomic interval data manipulation and restructuring in R programming language which is commonly used in bioinformatics, and therefore would be useful to bioinformaticians and genomic researchers.

Here we describe a patient who presented with a history of congenital diaphragmatic hernia, inguinal hernia, and recurrent umbilical hernia. She also has joint laxity, hypotonia, and dysmorphic features. A unifying diagnosis was not identified based on her clinical phenotype. As part of her evaluation through the Undiagnosed Diseases Network, trio whole-exome sequencing was performed. Pathogenic variants in FBN1 and TRPS1 were identified as causing two distinct autosomal dominant conditions, each with de novo inheritance. Fibrillin 1 (FBN1) mutations are associated with Marfan syndrome and a spectrum of similar phenotypes. TRPS1 mutations are associated with trichorhinophalangeal syndrome types I and III. Features of both conditions are evident in the patient reported here. Discrepant features of the conditions (e.g., stature) and the young age of the patient may have made a clinical diagnosis more difficult in the absence of exome-wide genetic testing.

Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.

© 2014 Nature America, Inc. All rights reserved., Polymorphous low-grade adenocarcinoma (PLGA) is the second most frequent type of malignant tumor of the minor salivary glands. We identified PRKD1 hotspot mutations encoding p.Glu710Asp in 72.9% of PLGAs but not in other salivary gland tumors. Functional studies demonstrated that this kinase-activating alteration likely constitutes a driver of PLGA., This work was supported in part by an IDEAS grant from Princess Margaret Hospital, the Head and Neck Translational Research Program (I.W., B.A.C., P.C.B. and J.D.M.), the Ontario Institute for Cancer Research and the government of Ontario (P.C.B. and J.D.M.) and by a Terry Fox Research Institute New Investigator Award (P.C.B.). C.H. and F.-F.L. acknowledge support from the Wharton family, Joe's Team, Gordon Tozer, the Campbell Family Institute for Cancer Research and the Ministry of Health and Long-Term Planning, Canada.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain similar to 8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

Polymorphous low-grade adenocarcinoma (PLGA) and cribriform adenocarcinoma of minor salivary gland (CAMSG) are low-grade carcinomas arising most often in oral cavity and oropharynx, respectively. Controversy exists as to whether these tumors represent separate entities or variants of one spectrum, as they appear to have significant overlap, but also clinicopathologic differences. As many salivary carcinomas harbor recurrent translocations, paired-end RNA sequencing and FusionSeq data analysis was applied for novel fusion discovery on two CAMSGs and two PLGAs. Validated rearrangements were then screened by fluorescence in situ hybridization (FISH) in 60 cases. Histologic classification was performed without knowledge of fusion status and included: 21 CAMSG, 18 classic PLGA, and 21 with "mixed/indeterminate" features. The RNAseq of 2 CAMSGs showed ARID1A-PRKD1 and DDX3X-PRKD1 fusions, respectively, while no fusion candidates were identified in two PLGAs. FISH for PRKD1 rearrangements identified 11 additional cases (22%), two more showing ARID1A-PRKD1 fusions. As PRKD2 and PRKD3 share similar functions with PRKD1 in the diacylglycerol and protein kinase C signal transduction pathway, we expanded the investigation for these genes by FISH. Six additional cases each showed PRKD2 and PRKD3 rearrangements. Of the 26 (43%) fusion-positive tumors, there were 16 (80%) CAMSGs and 9 (45%) indeterminate cases. A PRKD2 rearrangement was detected in one PLGA (6%). We describe novel and recurrent gene rearrangements in PRKD1-3 primarily in CAMSG, suggesting a possible pathogenetic dichotomy from "classic" PLGA. However, the presence of similar genetic findings in half of the indeterminate cases and a single PLGA suggests a possible shared pathogenesis for these tumor types.

Current clinical genomics assays primarily utilize short-read sequencing (SRS), which offers high throughput, high base accuracy, and low cost per base. SRS has, however, limited ability to evaluate tandem repeats, regions with high [GC] or [AT] content, highly polymorphic regions, highly paralogous regions, and large-scale structural variants. Long-read sequencing (LRS) has complementary strengths and offers a means to discover overlooked genetic variation in patients undiagnosed by SRS. To evaluate LRS, we selected a patient who presented with multiple neoplasia and cardiac myxomata suggestive of Carney complex for whom targeted clinical gene testing and whole genome SRS were negative. Low coverage whole genome LRS was performed on the PacBio Sequel system and structural variants were called, yielding 6,971 deletions and 6,821 insertions > 50bp. Filtering for variants that are absent in an unrelated control and that overlap a coding exon of a disease gene identified three deletions and three insertions. One of these, a heterozygous 2,184 bp deletion, overlaps the first coding exon of PRKAR1A, which is implicated in autosomal dominant Carney complex. This variant was confirmed by Sanger sequencing and was classified as pathogenic using standard criteria for the interpretation of sequence variants. This first successful application of whole genome LRS to identify a pathogenic variant suggests that LRS has significant potential to identify disease-causing structural variation. We recommend larger studies to evaluate the diagnostic yield of LRS, and the development of a comprehensive catalog of common human structural variation to support future studies.

Background Next-generation sequencing is making it critical to robustly and rapidly handle genomic ranges within standard pipelines. Standard use-cases include annotating sequence ranges with gene or other genomic annotation, merging multiple experiments together and subsequently quantifying and visualizing the overlap. The most widely-used tools for these tasks work at the command-line (e.g. BEDTools) and the small number of available R packages are either slow or have distinct semantics and features from command-line interfaces. Results To provide a robust R-based interface to standard command-line tools for genomic coordinate manipulation, we created bedr. This open-source R package can use either BEDTools or BEDOPS as a back-end and performs data-manipulation extremely quickly, creating R data structures that can be readily interfaced with existing computational pipelines. It includes data-visualization capabilities and a number of data-access functions that interface with standard databases like UCSC and COSMIC. Conclusions bedr package provides an open source solution to enable genomic interval data manipulation and restructuring in R programming language which is commonly used in bioinformatics, and therefore would be useful to bioinformaticians and genomic researchers. Electronic supplementary material The online version of this article (doi:10.1186/s13029-016-0059-5) contains supplementary material, which is available to authorized users.

To identify loci affecting the electrocardiographic QT interval, a measure of cardiac repolarisation associated with risk of ventricular arrhythmias and sudden cardiac death, we conducted a meta-analysis of three genome-wide association studies (GWAS) including 3,558 subjects from the TwinsUK and BRIGHT cohorts in the UK and the DCCT/EDIC cohort from North America. Five loci were significantly associated with QT interval at P−6. To validate these findings we performed an in silico comparison with data from two QT consortia: QTSCD (n = 15,842) and QTGEN (n = 13,685). Analysis confirmed the association between common variants near NOS1AP (P = 1.4×10−83) and the phospholamban (PLN) gene (P = 1.9×10−29). The most associated SNP near NOS1AP (rs12143842) explains 0.82% variance; the SNP near PLN (rs11153730) explains 0.74% variance of QT interval duration. We found no evidence for interaction between these two SNPs (P = 0.99). PLN is a key regulator of cardiac diastolic function and is involved in regulating intracellular calcium cycling, it has only recently been identified as a susceptibility locus for QT interval. These data offer further mechanistic insights into genetic influence on the QT interval which may predispose to life threatening arrhythmias and sudden cardiac death.

Congenital heart disease (CHD) has a complex genetic etiology, and recent studies suggest that high penetrance de novo mutations may account for only a small fraction of disease. In a multi-institutional cohort surveyed by exome sequencing, combining analysis of 987 individuals (discovery cohort of 59 affected trios and 59 control trios, and a replication cohort of 100 affected singletons and 533 unaffected singletons) we observe variation at novel and known loci related to a specific cardiac malformation the atrioventricular septal defect (AVSD). In a primary analysis, by combining developmental coexpression networks with inheritance modeling, we identify a de novo mutation in the DNA binding domain of NR1D2 (p.R175W). We show that p.R175W changes the transcriptional activity of Nr1d2 using an in vitro transactivation model in HUVEC cells. Finally, we demonstrate previously unrecognized cardiovascular malformations in the Nr1d2tm1-Dgen knockout mouse. In secondary analyses we map genetic variation to protein-interaction networks suggesting a role for two collagen genes in AVSD, which we corroborate by burden testing in a second replication cohort of 100 AVSDs and 533 controls (p = 8.37e-08). Finally, we apply a rare-disease inheritance model to identify variation in genes previously associated with CHD (ZFPM2, NSD1, NOTCH1, VCAN, and MYH6), cardiac malformations in mouse models (ADAM17, CHRD, IFT140, PTPRJ, RYR1 and ATE1), and hypomorphic alleles of genes causing syndromic CHD (EHMT1, SRCAP, BBS2, NOTCH2, and KMT2D) in 14 of 59 trios, greatly exceeding variation in control trios without CHD (p = 9.60e-06). In total, 32% of trios carried at least one putatively disease-associated variant across 19 loci,suggesting that inherited and de novo variation across a heterogeneous group of loci may contribute to disease risk., Author Summary Congenital heart disease (CHD) is a leading cause of childhood morbidity in the developed world. There are few prevalent clinical risk factors and though it is possible that up to 90% of risk for CHD may be genetic, the number of genes clinically associated with disease is small. Rather than grouping disparate CHD phenotypes as other studies have done, we studied a single specific malformation- the atrioventricular septal defect (AVSD). Instead of recurrent variation in a handful of genes, we observed de novo and inherited variation in 19 genes associated with human disease, syndromic loci, and genes implicated in cardiac development by mouse knockout. The number of loci identified support the longstanding hypothesis of a complex oligogenic inheritance for a single malformation and suggest that analyses of CHD data to include inherited variation may uncover additional loci contributing risk for cardiac malformations.

Current medical records are rigid with regards to emerging big biomedical data. Examples of poorly integrated big data that already exist in clinical practice include whole genome sequencing and wearable sensors for real time monitoring. Genome sequencing enables conventional diagnostic interrogation and forms the fundamental baseline for precision health throughout a patient’s lifetime. Mobile sensors enable tailored monitoring regimes for both reducing risk through precision health interventions and acute condition surveillance. In order to address the absence of these data in the Electronic Medical Record (EMR), we worked with the SAP Personalized Medicine team to re-envision a modern medical record with these components. The pilot project used 37 patient families with complex medical records, whole genome sequencing and some level of wearable monitoring. Core functionality included patient timelines with integrated text analytics, personalized genomic curation and wearable alerts. The current phase is being rolled out to over 1500 patients in clinics across the hospital system. While fundamentally research, we believe this proof of principle platform is the first of its kind and represents the future of data driven clinical medicine.

Background Carcinomas of the oral cavity, pharynx and larynx are referred to as head and neck cancers (HNC); together they account for 2⿿3% of all newly diagnosed cancers in North America. Between 40⿿50% of HNC are early diagnosed at stages I⿿II. The 5-year and 10-year relative survival rates are 61% and 50%, respectively. Germline genetic sequence variants (GSV) have become increasingly found to have prognostic implications in a variety of cancers. Identifying these variants may have important clinical and biological implications. Methods We conducted a genome-wide association study (GWAS) in 531 Stage I⿿II radiation-treated HNC patients (originally recruited for α-tocopherol/β-carotene placebo-controlled secondary prevention study) and used a replication cohort of 566 HNC patients of all stages, of mostly non-HPV-related cancers. Survival rates were estimated by the Kaplan-Meier method. Cox proportional hazards models adjusted for potential clinical factors and principal components were used to test for associations between the GSV and overall survival (OS) in these tumors. Results The median follow-up time for OS was 9.21 years (GWAS cohort) and 2.37 years (replication cohort). In both cohorts, CACNA2D1 :rs2299187, ESRRG :rs946465 and ESRRG: rs1416612 were each individually significantly associated with survival. In silico analysis of ESRRG :rs946465 identifies that it produces a splice variant in ESRRG. Variant alleles of CACNA2D1 :rs2299187 and ESRRG: rs946465 were associated with higher expression of the corresponding protein. Conclusions Putatively functional polymorphisms in the MAP-Kinase and estrogen pathways, identified through GWAS and replicated in an independent dataset were associated with the survival of HNC patients.

Sports genetics can take advantage of lessons learned from human disease genetics. By righting past mistakes and increasing scientific rigor, we can magnify the breadth and depth of knowledge in the field. We present an outline of challenges facing sports genetics in the light of experiences from medical research. Sports performance is complex, resulting from a combination of a wide variety of different traits and attributes. Improving sports genetics will foremost require analyses based on detailed phenotyping. To find widely valid, reproducible common variants associated with athletic phenotypes, study sample sizes must be dramatically increased. One paradox is that in order to confirm relevance, replications in specific populations must be undertaken. Family studies of athletes may facilitate the discovery of rare variants with large effects on athletic phenotypes. The complexity of the human genome, combined with the complexity of athletic phenotypes, will require additional metadata and biological validation to identify a comprehensive set of genes involved. Analysis of personal genetic and multiomic profiles contribute to our conceptualization of precision medicine; the same will be the case in precision sports science. In the refinement of sports genetics it is essential to evaluate similarities and differences between sexes and among ethnicities. Sports genetics to date have been hampered by small sample sizes and biased methodology, which can lead to erroneous associations and overestimation of effect sizes. Consequently, currently available genetic tests based on these inherently limited data cannot predict athletic performance with any accuracy.

Item does not contain fulltext The ASXL genes (ASXL1, ASXL2, and ASXL3) participate in body patterning during embryogenesis and encode proteins involved in epigenetic regulation and assembly of transcription factors to specific genomic loci. Germline de novo truncating variants in ASXL1 and ASXL3 have been respectively implicated in causing Bohring-Opitz and Bainbridge-Ropers syndromes, which result in overlapping features of severe intellectual disability and dysmorphic features. ASXL2 has not yet been associated with a human Mendelian disorder. In this study, we performed whole-exome sequencing in six unrelated probands with developmental delay, macrocephaly, and dysmorphic features. All six had de novo truncating variants in ASXL2. A careful review enabled the recognition of a specific phenotype consisting of macrocephaly, prominent eyes, arched eyebrows, hypertelorism, a glabellar nevus flammeus, neonatal feeding difficulties, hypotonia, and developmental disabilities. Although overlapping features with Bohring-Opitz and Bainbridge-Ropers syndromes exist, features that distinguish the ASXL2-associated condition from ASXL1- and ASXL3-related disorders are macrocephaly, absence of growth retardation, and more variability in the degree of intellectual disabilities. We were also able to demonstrate with mRNA studies that these variants are likely to exert a dominant-negative effect, given that both alleles are expressed in blood and the mutated ASXL2 transcripts escape nonsense-mediated decay. In conclusion, de novo truncating variants in ASXL2 underlie a neurodevelopmental syndrome with a clinically recognizable phenotype. This report expands the germline disorders that are linked to the ASXL genes.

Background— Elevated serum soluble E-selectin levels have been associated with a number of diseases. Although E-selectin levels are heritable, little is known about the specific genetic factors involved. E-selectin levels have been associated with the ABO blood group phenotype. Methods and Results— We performed a high-resolution genome-wide association study of serum soluble E-selectin levels in 685 white individuals with type 1 diabetes from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Intervention and Complications (EDIC) study to identify major loci influencing levels. Highly significant evidence for association ( P =10 −29 ) was observed for rs579459 near the ABO blood group gene, accounting for 19% of the variance in E-selectin levels. Levels of E-selectin were higher in O/O than O/A heterozygotes, which were likewise higher than A/A genotypes. Analysis of subgroups of A alleles reveals heterogeneity in the association, and even after this was accounted for, an intron 1 SNP remained significantly associated. We replicate the ABO association in nondiabetic individuals. Conclusion— ABO is a major locus for serum soluble E-selectin levels. We excluded population stratification, fine-mapped the association to sub-A alleles, and also document association with additional variation in the ABO region.

OBJECTIVE Glycemia is a major risk factor for the development of long-term complications in type 1 diabetes; however, no specific genetic loci have been identified for glycemic control in individuals with type 1 diabetes. To identify such loci in type 1 diabetes, we analyzed longitudinal repeated measures of A1C from the Diabetes Control and Complications Trial. RESEARCH DESIGN AND METHODS We performed a genome-wide association study using the mean of quarterly A1C values measured over 6.5 years, separately in the conventional (n = 667) and intensive (n = 637) treatment groups of the DCCT. At loci of interest, linear mixed models were used to take advantage of all the repeated measures. We then assessed the association of these loci with capillary glucose and repeated measures of multiple complications of diabetes. RESULTS We identified a major locus for A1C levels in the conventional treatment group near SORCS1 (10q25.1, P = 7 × 10−10), which was also associated with mean glucose (P = 2 × 10−5). This was confirmed using A1C in the intensive treatment group (P = 0.01). Other loci achieved evidence close to genome-wide significance: 14q32.13 (GSC) and 9p22 (BNC2) in the combined treatment groups and 15q21.3 (WDR72) in the intensive group. Further, these loci gave evidence for association with diabetic complications, specifically SORCS1 with hypoglycemia and BNC2 with renal and retinal complications. We replicated the SORCS1 association in Genetics of Diabetes in Kidneys (GoKinD) study control subjects (P = 0.01) and the BNC2 association with A1C in nondiabetic individuals. CONCLUSIONS A major locus for A1C and glucose in individuals with diabetes is near SORCS1. This may influence the design and analysis of genetic studies attempting to identify risk factors for long-term diabetic complications.

OBJECTIVE Despite extensive evidence for genetic susceptibility to diabetic nephropathy, the identification of susceptibility genes and their variants has had limited success. To search for genes that contribute to diabetic nephropathy, a genome-wide association scan was implemented on the Genetics of Kidneys in Diabetes collection. RESEARCH DESIGN AND METHODS We genotyped ∼360,000 single nucleotide polymorphisms (SNPs) in 820 case subjects (284 with proteinuria and 536 with end-stage renal disease) and 885 control subjects with type 1 diabetes. Confirmation of implicated SNPs was sought in 1,304 participants of the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, a long-term, prospective investigation of the development of diabetes-associated complications. RESULTS A total of 13 SNPs located in four genomic loci were associated with diabetic nephropathy with P < 1 × 10−5. The strongest association was at the FRMD3 (4.1 protein ezrin, radixin, moesin [FERM] domain containing 3) locus (odds ratio [OR] = 1.45, P = 5.0 × 10−7). A strong association was also identified at the CARS (cysteinyl-tRNA synthetase) locus (OR = 1.36, P = 3.1 × 10−6). Associations between both loci and time to onset of diabetic nephropathy were supported in the DCCT/EDIC study (hazard ratio [HR] = 1.33, P = 0.02, and HR = 1.32, P = 0.01, respectively). We demonstratedexpression of both FRMD3 and CARS in human kidney. CONCLUSIONS We identified genetic associations for susceptibility to diabetic nephropathy at two novel candidate loci near the FRMD3 and CARS genes. Their identification implicates previously unsuspected pathways in the pathogenesis of this important late complication of type 1 diabetes.

As whole exome sequencing (WES) and whole genome sequencing (WGS) transition from research tools to clinical diagnostic tests, it is increasingly critical for sequencing methods and analysis pipelines to be technically accurate. The Genome in a Bottle Consortium has recently published a set of benchmark SNV, indel, and homozygous reference genotypes for the pilot whole genome NIST Reference Material based on the NA12878 genome. We examine the relationship between human genome complexity and genes/variants reported to be associated with human disease. Specifically, we map regions of medical relevance to benchmark regions of high or low confidence. We use benchmark data to assess the sensitivity and positive predictive value of two representative sequencing pipelines for specific classes of variation. We observe that the accuracy of a variant call depends on the genomic region, variant type, and read depth, and varies by analytical pipeline. We find that most false negative WGS calls result from filtering while most false negative WES variants relate to poor coverage. We find that only 74.6 % of the exonic bases in ClinVar and OMIM genes and 82.1 % of the exonic bases in ACMG-reportable genes are found in high-confidence regions. Only 990 genes in the genome are found entirely within high-confidence regions while 593 of 3,300 ClinVar/OMIM genes have less than 50 % of their total exonic base pairs in high-confidence regions. We find greater than 77 % of the pathogenic or likely pathogenic SNVs currently in ClinVar fall within high-confidence regions. We identify sites that are prone to sequencing errors, including thousands present in publicly available variant databases. Finally, we examine the clinical impact of mandatory reporting of secondary findings, highlighting a false positive variant found in BRCA2. Together, these data illustrate the importance of appropriate use and continued improvement of technical benchmarks to ensure accurate and judicious interpretation of next-generation DNA sequencing results in the clinical setting.

Background: Technological advances have greatly reduced the cost of whole genome sequencing. For single individuals clinical application is apparent, while exome sequencing in tens of thousands of people has allowed a more global view of genetic variation that can inform interpretation of specific variants in individuals. We hypothesized that genome sequencing of patients with monogenic cardiomyopathy would facilitate discovery of genetic modifiers of phenotype. Methods and Results: We identified 48 individuals diagnosed with cardiomyopathy and with putative mutations in MYH7, the gene encoding beta myosin heavy chain. We carried out whole genome sequencing and applied a newly developed analytical pipeline optimized for discovery of genes modifying severity of clinical presentation and outcomes. Using a combination of external priors and rare variant burden tests we scored genes as potential modifiers. There were 96 genes that reached a modifier score of 6 out of 12 or better (9=2, 8=8, 7=17, 6=69). We identified NCKAP1, a gene that regulates actin filament dynamics, and CAMSAP1, a calmodulin regulate gene that regulates microtubule dynamics, as top scoring modifiers of hypertrophic cardiomyopathy phenotypes (score=9) while LDB2, RYR2, FBN1 and ATP1A2 had modifier scores of 8. Of the top scoring genes, 21 out of 96 were identified as candidates a priori. Our candidate prioritization scheme identified the previously described modifiers of cardiomyopathy phenotype, FHOD3 and MYBPC3, as top scoring genes. We identified structural variants in 21 clinically sequenced cardiomyopathy associated genes, 13 of which were at less than 10% frequency. Copy number variants in ILK and CSRP3 were nominally associated with ejection fraction (p=0.03), while 8 genes showed copy gains (GLA, FKTN, SGCD, TTN, SOS1, ANKRD1, VCL and NEBL). Structural variants were found in CSRP3, MYL3 and TNNC1, all of which have been implicated as causative for HCM. Conclusion: Evaluation of the whole genome sequence, even in the case of putatively monogenic disease, leads to important diagnostic and scientific insights not revealed by panel-based sequencing.

Cervical cancer remains the third most frequently diagnosed and fourth leading cause of cancer death in women worldwide. We sought to develop a micro-RNA signature that was prognostic for disease-free survival, which could potentially allow tailoring of treatment for cervical cancer patients. A candidate prognostic 9-micro-RNA signature set was identified in the training set of 79 frozen specimens. However, three different approaches to validate this signature in an independent cohort of 87 patients with formalin-fixed paraffin-embedded (FFPE) specimens, were unsuccessful. There are several challenges and considerations associated with developing a prognostic micro-RNA signature for cervical cancer, namely: tumour heterogeneity, lack of concordance between frozen and FFPE specimens, and platform selection for global micro-RNA expression profiling in this disease. Our observations provide an important cautionary tale for future miRNA signature studies for cervical cancer, which can also be potentially applicable to miRNA profiling studies involving other types of human malignancies.

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis.

Polymorphous low-grade adenocarcinoma (PLGA) and cribriform adenocarcinoma of minor salivary gland (CAMSG) are low-grade carcinomas arising most often in oral cavity and oropharynx, respectively. Controversy exists as to whether these tumors represent separate entities or variants of one spectrum, as they appear to have significant overlap, but also clinicopathologic differences. As many salivary carcinomas harbor recurrent translocations, paired-end RNA sequencing and FusionSeq data analysis was applied for novel fusion discovery on two CAMSGs and two PLGAs. Validated rearrangements were then screened by fluorescence in situ hybridization (FISH) in 60 cases. Histologic classification was performed without knowledge of fusion status and included: 21 CAMSG, 18 classic PLGA, and 21 with "mixed/indeterminate" features. The RNAseq of 2 CAMSGs showed ARID1A-PRKD1 and DDX3X-PRKD1 fusions, respectively, while no fusion candidates were identified in two PLGAs. FISH for PRKD1 rearrangements identified 11 additional cases (22%), two more showing ARID1A-PRKD1 fusions. As PRKD2 and PRKD3 share similar functions with PRKD1 in the diacylglycerol and protein kinase C signal transduction pathway, we expanded the investigation for these genes by FISH. Six additional cases each showed PRKD2 and PRKD3 rearrangements. Of the 26 (43%) fusion-positive tumors, there were 16 (80%) CAMSGs and 9 (45%) indeterminate cases. A PRKD2 rearrangement was detected in one PLGA (6%). We describe novel and recurrent gene rearrangements in PRKD1-3 primarily in CAMSG, suggesting a possible pathogenetic dichotomy from "classic" PLGA. However, the presence of similar genetic findings in half of the indeterminate cases and a single PLGA suggests a possible shared pathogenesis for these tumor types.

We describe the presentation, management, and clinical outcome of a massive acinic cell carcinoma of the parotid gland. The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. This study provides the first insights into the genetic underpinnings of this cancer. Future large-scale efforts will be necessary to define the mutational landscape of salivary gland malignancies to identify therapeutic targets and biomarkers of treatment failure.

Profiling of mRNA abundances with high-throughput platforms such as microarrays and RNA-seq has become an important tool in both basic and biomedical research. However, these platforms remain prone to systematic errors and have challenges in clinical and industrial applications. As a result, it is standard practice to validate a subset of key results using alternate technologies. Similarly, clinical and industrial applications typically involve transitions from a high-throughput discovery platform to medium-throughput validation ones. These medium-throughput validation platforms have high technical reproducibility and reduced sample input needs, and low sensitivity to sample quality (e.g., for processing FFPE specimens). Unfortunately, while medium-throughput platforms have proliferated, there are no comprehensive comparisons of them. Here we fill that gap by comparing two key medium-throughput platforms—NanoString's nCounter Analysis System and ABI's OpenArray System—to gold-standard quantitative real-time RT-PCR. We quantified 38 genes and positive and negative controls in 165 samples. Signal:noise ratios, correlations, dynamic range, and detection accuracy were compared across platforms. All three measurement technologies showed good concordance, but with divergent price/time/sensitivity trade-offs. This study provides the first detailed comparison of medium-throughput RNA quantification platforms and provides a template and a standard data set for the evaluation of additional technologies.

We identified genetic predictors of diabetes associated erectile dysfunction using genome-wide and candidate gene approaches in a cohort of men with type 1 diabetes.We examined 528 white men with type 1 diabetes, including 125 with erectile dysfunction, from DCCT (Diabetes Control and Complications Trial) and its observational followup, the EDIC (Epidemiology of Diabetes Interventions and Complications) study. Erectile dysfunction was identified from a single International Index of Erectile Function item. A Human1M BeadChip (Illumina®) was used for genotyping. A total of 867,125 single nucleotide polymorphisms were subjected to analysis. Whole genome and candidate gene approaches were used to test the hypothesis that genetic polymorphisms may predispose men with type 1 diabetes to erectile dysfunction. Univariate and multivariate models were used, controlling for age, HbA1c, diabetes duration and prior randomization to intensive or conventional insulin therapy during DCCT. A stratified false discovery rate was used to perform the candidate gene approach.Two single nucleotide polymorphisms located on chromosome 3 in 1 genomic loci were associated with erectile dysfunction with p1 × 10(-6), including rs9810233 with p = 7 × 10(-7) and rs1920201 with p = 9 ×10(-7). The nearest gene to these 2 single nucleotide polymorphisms is ALCAM. Genetic association results at these loci were similar on univariate and multivariate analysis. No candidate genes met the criteria for statistical significance.Two single nucleotide polymorphisms, rs9810233 and rs1920101, which are 25 kb apart, are associated with erectile dysfunction, although they do not meet the standard genome-wide association study significance criterion of p5 × 10(-8). Other studies with larger sample sizes are required to determine whether ALCAM represents a novel gene in the pathogenesis of diabetes associated erectile dysfunction.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ∼2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2×10−8) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0×10−9). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1×10−7), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN., Author Summary The global prevalence of diabetes has reached epidemic proportions, constituting a major health care problem worldwide. Diabetic kidney disease, or diabetic nephropathy (DN)—the major long term microvascular complication of diabetes—is associated with excess mortality among patients with type 1 diabetes. Even though DN has been shown to cluster in families, the underlying genetic and molecular pathways remain poorly defined. We have undertaken the largest genome-wide association study and meta-analysis to date on DN and on its most severe form of kidney disease, end-stage renal disease (ESRD). We identified new loci significantly associated with diabetic ESRD: AFF3 and an intergenic locus on chromosome 15q26 residing between RGMA and MCTP2. Our functional analyses suggest that AFF3 influences renal tubule fibrosis, a pathological hallmark of severe DN. Another locus in ERBB4 was suggestively associated with DN and resides in the same intronic region as a variant affecting the expression of ERBB4. Subsequent pathway analysis of the genes co-expressed with ERBB4 indicated involvement of fibrosis.

The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies.