Disease risk estimation plays an important role in disease prevention. Many studies have found that the ability to predict risk improves as the number of risk single-nucleotide polymorphisms (SNPs) in the risk model increases. However, the width of the confidence interval of the risk estimate is often not considered in the evaluation of the risk model. Here, we explore how the risk and the confidence interval width change as more SNPs are added to the model in the order of decreasing effect size, using both simulated data and real data from studies of abdominal aortic aneurysms and age-related macular degeneration. Our results show that confidence interval width is positively correlated with model size and the majority of the bigger models have wider confidence interval widths than smaller models. Once the model size is bigger than a certain level, the risk does not shift markedly, as 100% of the risk estimates of the one-SNP-bigger models lie inside the confidence interval of the one-SNP-smaller models. We also created a confidence interval-augmented reclassification table. It shows that both more effective SNPs with larger odds ratios and less effective SNPs with smaller odds ratios contribute to the correct decision of whom to screen. The best screening strategy is selected and evaluated by the net benefit quantity and the reclassification rate. We suggest that individuals whose upper bound of their risk confidence interval is above the screening threshold, which corresponds to the population prevalence of the disease, should be screened.

Abstract Introduction We have previously identified a rare subpopulation of variant human mammary epithelial cells (vHMEC) with repressed p16INK4A that exist in disease-free women yet display premalignant properties, suggesting that they have engaged the process of malignant transformation. In order to gain insight into the molecular alterations required for vHMEC to progress to malignancy, and to characterize the epigenetic events associated with early progression, we examined the effect of oncogenic stress on the behavior of these cells. Methods HMEC that express p16INK4A and vHMEC that do not, were transduced with constitutively active Ha-rasV12 and subsequently exposed to serum to determine whether signals from the cellular microenvironment could cooperate with ras to promote the malignant transformation of vHMEC. Epigenetic alterations were assessed using methylation-specific polymerase chain reaction (PCR). Results vHMEC expressing Ha-rasV12 (vHMEC-ras) bypassed the classic proliferative arrest that has been previously documented in normal fibroblasts following oncogenic stress, and that we also observe here in normal HMEC. Moreover, vHMEC-ras cells exhibited many additional alterations that are observed during progression to malignancy such as the generation of chromosomal abnormalities, upregulation of telomerase activity, immortalization following exposure to serum, and anchorage-independent growth, but they did not form tumors following orthotopic injection in vivo. Associated with their early progression to malignancy was an increase in the number of genes methylated, two of which (RASSF1A and SFRP1) were also methylated in other immortalized mammary cell lines as well as in breast cancer cells and tissues. Conclusions We have characterized a mammary progression model that recapitulates molecular and methylation alterations observed in many breast cancers. Our data suggest that concomitant methylation of RASSF1A and SFRP1 marks an early event in mammary transformation and may thus have prognostic potential.

Autism spectrum disorder is a complex trait with a high degree of heritability as well as documented susceptibility from environmental factors. In this study the contributions of copy number variation, exposure to air pollutants, and the interaction between the two on autism risk, were evaluated in the population-based case-control Childhood Autism Risks from Genetics and Environment (CHARGE) Study. For the current investigation, we included only those CHARGE children (a) who met criteria for autism or typical development and (b) for whom our team had conducted both genetic evaluation of copy number burden and determination of environmental air pollution exposures based on mapping addresses from the pregnancy and early childhood. This sample consisted of 158 cases of children with autism and 147 controls with typical development. Multiple logistic regression models were fit with and without environmental variable-copy number burden interactions. We found no correlation between average air pollution exposure from conception to age 2 years and the child's CNV burden. We found a significant interaction in which a 1SD increase in duplication burden combined with a 1SD increase in ozone exposure was associated with an elevated autism risk (OR 3.4, P < 0.005) much greater than the increased risks associated with either genomic duplication (OR 1.85, 95% CI 1.25-2.73) or ozone (OR 1.20, 95% CI 0.93-1.54) alone. Similar results were obtained when CNV and ozone were dichotomized to compare those in the top quartile relative to those having a smaller CNV burden and lower exposure to ozone, and when exposures were assessed separately for pregnancy, the first year of life, and the second year of life. No interactions were observed for other air pollutants, even those that demonstrated main effects; ozone tends to be negatively correlated with the other pollutants examined. While earlier work has demonstrated interactions between the presence of a pathogenic CNV and an environmental exposure [Webb et al., 2016], these findings appear to be the first indication that global copy number variation may increase susceptibility to certain environmental factors, and underscore the need to consider both genomics and environmental exposures as well as the mechanisms by which each may amplify the risks for autism associated with the other. Autism Res 2017, 10: 1470-1480. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

We present a catalog of likely astrophysical neutrino track-like events from the IceCube Neutrino Observatory. IceCube began reporting likely astrophysical neutrinos in 2016, and this system was updated in 2019. The catalog presented here includes events that were reported in real time since 2019, as well as events identified in archival data samples starting from 2011. We report 275 neutrino events from two selection channels as the first entries in the catalog, the IceCube Event Catalog of Alert Tracks, which will see ongoing extensions with additional alerts. The Gold and Bronze alert channels respectively provide neutrino candidates with a 50% and 30% probability of being astrophysical, on average assuming an astrophysical neutrino power-law energy spectral index of 2.19. For each neutrino alert, we provide the reconstructed energy, direction, false-alarm rate, probability of being astrophysical in origin, and likelihood contours describing the spatial uncertainty in the alert's reconstructed location. We also investigate a directional correlation of these neutrino events with gamma-ray and X-ray catalogs, including 4FGL, 3HWC, TeVCat, and Swift-BAT.

Adopting the Standard Halo Model (SHM) of an isotropic Maxwellian velocity distribution for dark matter (DM) particles in the Galaxy, the most stringent current constraints on their spin-dependent scattering cross-section with nucleons come from the IceCube neutrino observatory and the PICO-60 C 3F 8 superheated bubble chamber experiments. The former is sensitive to high energy neutrinos from the self-annihilation of DM particles captured in the Sun, while the latter looks for nuclear recoil events from DM scattering off nucleons. Although slower DM particles are more likely to be captured by the Sun, the faster ones are more likely to be detected by PICO. Recent N-body simulations suggest significant deviations from the SHM for the smooth halo component of the DM, while observations hint at a dominant fraction of the local DM being in substructures. We use the method of Ferrer et al. (JCAP 1509: 052, 2015) to exploit the complementarity between the two approaches and derive conservative constraints on DM-nucleon scattering. Our results constrain σSD≲ 3 × 10 - 39cm 2 (6 × 10 - 38cm 2) at ≳ 90 % C.L. for a DM particle of mass 1 TeV annihilating into τ+τ- (bb¯) with a local density of ρDM=0.3GeV/cm3. The constraints scale inversely with ρDM and are independent of the DM velocity distribution.

The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

We present two searches for IceCube neutrino events coincident with 28 fast radio bursts (FRBs) and 1 repeating FRB. The first improves on a previous IceCube analysis - searching for spatial and temporal correlation of events with FRBs at energies greater than roughly 50 GeV - by increasing the effective area by an order of magnitude. The second is a search for temporal correlation of MeV neutrino events with FRBs. No significant correlation is found in either search; therefore, we set upper limits on the time-integrated neutrino flux emitted by FRBs for a range of emission timescales less than one day. These are the first limits on FRB neutrino emission at the MeV scale, and the limits set at higher energies are an order-of-magnitude improvement over those set by any neutrino telescope.

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV-100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%-20% compared to current maximum likelihood techniques in the energy range of 1 GeV-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

The Sun provides an excellent target for studying spin-dependent dark matter-proton scattering due to its high matter density and abundant hydrogen content. Dark matter particles from the Galactic halo can elastically interact with Solar nuclei, resulting in their capture and thermalization in the Sun. The captured dark matter can annihilate into Standard Model particles including an observable flux of neutrinos. We present the results of a search for low-energy (<500 GeV) neutrinos correlated with the direction of the Sun using 7 years of IceCube data. This work utilizes, for the first time, new optimized cuts to extend IceCube's sensitivity to dark matter mass down to 5 GeV. We find no significant detection of neutrinos from the Sun. Our observations exclude capture by spin-dependent dark matter-proton scattering with cross section down to a few times 10-41 cm2, assuming there is equilibrium with annihilation into neutrinos/antineutrinos for dark matter masses between 5 GeV and 100 GeV. These are the strongest constraints at GeV energies for dark matter annihilation directly to neutrinos.