34 research outputs found

    Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis

    Get PDF
    Background: Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood. Methods: Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval). Results: We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals. Conclusions: Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation

    New insights into the genetic etiology of Alzheimer's disease and related dementias

    Get PDF
    Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

    Variations in reflectance with seasonality and viewing geometry: implications for semi-arid vegetation mapping with MISR data

    No full text
    The purpose of this article is to understand the effect of multi-temporalmulti-angle data on vegetation community type mapping in desert regions. Based on data from the multi-angle imaging spectroradiometer (MISR), a set of 46 multi-temporal classification experiments were carried out in the Jornada Experimental Range in New Mexico, USA. Besides multi-angle observations, bidirectional reflectance distribution function (BRDF) model parameters were also used as input data for the classifications. The experiments used two widely accepted BRDF models, the Rahman-Pinty-Verstraete (RPV) model and the Ross-thin Li-sparse reciprocal (RTnLS) model. The experiments show that multi-temporal multi-angle classifications can yield a more accurate mapping than multi-temporal nadir classifications, and multi-temporal BRDF model parameters combined with a single nadir image can provide an accuracy roughly the same as all multi-temporal multi-angle observations for the vegetation mapping. These findings opened not only a path of reducing data dimensionality for multi-temporal multi-angle classifications, but also a way of merging products of both MISR and moderate resolution imaging spectroradiometer (MODIS) to improve semi-arid vegetation mapping
    corecore