Comparison of statistical approaches to rare variant analysis for quantitative traits

With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants

The Hidden Inconsistencies Introduced by Predictive Algorithms in Judicial Decision Making

Algorithms, from simple automation to machine learning, have been introduced into judicial contexts to ostensibly increase the consistency and efficiency of legal decision making. In this paper, we describe four types of inconsistencies introduced by risk prediction algorithms. These inconsistencies threaten to violate the principle of treating similar cases similarly and often arise from the need to operationalize legal concepts and human behavior into specific measures that enable the building and evaluation of predictive algorithms. These inconsistencies, however, are likely to be hidden from their end-users: judges, parole officers, lawyers, and other decision-makers. We describe the inconsistencies, their sources, and propose various possible indicators and solutions. We also consider the issue of inconsistencies due to the use of algorithms in light of current trends towards more autonomous algorithms and less human-understandable behavioral big data. We conclude by discussing judges and lawyers' duties of technological ("algorithmic") competence and call for greater alignment between the evaluation of predictive algorithms and corresponding judicial goals

Exome Sequence association Study of Levels and Longitudinal Change of Cardiovascular Risk Factor Phenotypes in European americans and african americans From the atherosclerosis Risk in Communities Study

Cardiovascular disease (CVD) is responsible for 31% of all deaths worldwide. Among CVD risk factors are age, race, increased systolic blood pressure (BP), and dyslipidemia. Both BP and blood lipids levels change with age, with a dose-dependent relationship between the cumulative exposure to hyperlipidemia and the risk of CVD. We performed an exome sequence association study using longitudinal data with up to 7805 European Americans (EAs) and 3171 African Americans (AAs) from the Atherosclerosis Risk in Communities (ARIC) study. We assessed associations of common (minor allele frequency \u3e 5%) nonsynonymous and splice-site variants and gene-based sets of rare variants with levels and with longitudinal change of seven CVD risk factor phenotypes (BP traits: systolic BP, diastolic BP, pulse pressure; lipids traits: triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]). Furthermore, we investigated the relationship of the identified variants and genes with select CVD endpoints. We identified two novel genes: DCLK3 associated with the change of HDL-C levels in AAs and RAB7L1 associated with the change of LDL-C levels in EAs. RAB7L1 is further associated with an increased risk of heart failure in ARIC EAs. Investigation of the contribution of genetic factors to the longitudinal change of CVD risk factor phenotypes promotes our understanding of the etiology of CVD outcomes, stressing the importance of incorporating the longitudinal structure of the cohort data in future analyses

Microscopic Description of Band Structure at Very Extended Shapes in the A ~ 110 Mass Region

Recent experiments have confirmed the existence of rotational bands in the A \~ 110 mass region with very extended shapes lying between super- and hyper-deformation. Using the projected shell model, we make a first attempt to describe quantitatively such a band structure in 108Cd. Excellent agreement is achieved in the dynamic moment of inertia J(2) calculation. This allows us to suggest the spin values for the energy levels, which are experimentally unknown. It is found that at this large deformation, the sharply down-sloping orbitals in the proton i_{13/2} subshell are responsible for the irregularity in the experimental J(2), and the wave functions of the observed states have a dominant component of two-quasiparticles from these orbitals. Measurement of transition quadrupole moments and g-factors will test these findings, and thus can provide a deeper understanding of the band structure at very extended shapes.Comment: 4 pages, 3 eps figures, final version accepted by Phys. Rev. C as a Rapid Communicatio

Porphyrin-based metal–organic frameworks for neuromorphic electronics

Porphyrin-based metal–organic frameworks (PP-MOFs) have some special features beyond ordinary MOFs, including superior optoelectronic characteristics, the ability to form 2D layered structure, and customizability, which prompt the increasing attention of PP-MOFs in the field of neuromorphic electronics. The related application research is in the initial stage, and a timely summary and guidance are necessary. The PP-MOFs fabrication should be shifted from powder synthesis in a chemistry laboratory to high-quality film preparation under a clean environment to ensure device performance. This article highlights the PP-MOFs film preparation methods and the application advances in neuromorphic electronics, performs comparative analysis in detail, and puts forward the challenges and future research directions, with the aim to attract the attention of experts in various areas (e.g., chemists, materials scientists, and engineers) and promote the application of PP-MOFs in neuromorphic electronics

Solid-State Spun Fibers from 1 mm Long Carbon Nanotube Forests Synthesized by Water-Assisted Chemical Vapor Deposition

In this work, we report continuous carbon nanotube fibers dry-drawn directly from water-assisted CVD grown forests with millimeter scale length. As-drawn nanotube fibers exist as aerogel and can be transformed into more compact fibers through twisting or densification with a volatile liquid. Nanotube fibers are characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Raman microscopy and wide-angle X-ray diffraction (WAXD). Mechanical behavior and electrical conductivity of the post-treated nanotube fibers are investigated

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Are there Social Spillovers in Consumers’ Security Assessments of Payment Instruments?

Even though security of payments has long been identified as an important aspect of the consumer payment experience, recent literature fails to appropriately assess the extent of social spillovers among payment users. We test for the existence and importance of such spillovers by analyzing whether social influence affects consumers’ perceptions of the security of payment instruments. Based on a 2008–2014 annual panel data survey of consumers, we find strong evidence of social spillovers in payment markets: others’ perceptions of security of payment instruments exert a positive influence on one’s own payment security perceptions. The significant and robust results imply that a consumer’s assessments of security converge to his peers’ average assessment: a 10 percent change in the divergence between one’s own security rating and peers’ average rating will result in a 7 percent change in one’s own rating in the next period. The results are robust to many specifications and do not change when we control for actual fraud or crime data. Our results indicate that spillovers rather than reflection appear to be the cause, although separating the two causes is very difficult (Manski 1993). In particular, the spillovers are stronger for people who experience an exogenous shock to security perception, people who have more social interactions, and younger consumers, who are more likely to be influenced by social media. We also examine the effects of social spillovers on payment behavior (that is, on decisions regarding payment adoption and use). Our results indicate that social spillovers have a rather limited impact on payment behavior, as others’ perceptions seem to affect one’s own payment behavior mainly indirectly through the effect on one’s own perceptions