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The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world(1). Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, includingPIEZO1on varicose veins,COL6A1on corneal resistance,MEPEon bone density, andIQGAP2andGMPRon blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenicBRCA1andBRCA2variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community. <br /> Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.
Inositol polyphosphates, such as inositol trisphosphate, are pivotal intracellular signaling molecules in eukaryotic cells. In higher plants the mechanism for the regulation of the type and the level of these signaling molecules is poorly understood. In this study we investigate the physiological function of an Arabidopsis (Arabidopsis thaliana) gene encoding inositol polyphosphate kinase (AtIPK2alpha), which phosphorylates inositol 1,4,5-trisphosphate successively at the D-6 and D-3 positions, and inositol 1,3,4,5-tetrakisphosphate at D-6, resulting in the generation of inositol 1,3,4,5,6-pentakisphosphate. Semiquantitative reverse transcription-PCR and promoter-beta-glucuronidase reporter gene analyses showed that AtIPK2alpha is expressed in various tissues, including roots and root hairs, stem, leaf, pollen grains, pollen tubes, the flower stigma, and siliques. Transgenic Arabidopsis plants expressing the AtIPK2alpha antisense gene under its own promoter were generated. Analysis of several independent transformants exhibiting strong reduction in AtIPK2alpha transcript levels showed that both pollen germination and pollen tube growth were enhanced in the antisense lines compared to wild-type plants, especially in the presence of nonoptimal low Ca2+ concentrations in the culture medium. Furthermore, root growth and root hair development were also stimulated in the antisense lines, in the presence of elevated external Ca2+ concentration or upon the addition of EGTA. In addition, seed germination and early seedling growth was stimulated in the antisense lines. These observations suggest a general and important role of AtIPK2alpha, and hence inositol polyphosphate metabolism, in the regulation of plant growth most likely through the regulation of calcium signaling, consistent with the well-known function of inositol trisphosphate in the mobilization of intracellular calcium stores
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.