Background
Suicide is among the leading causes of death in children and adolescents. There are well-known risk factors of suicide, including childhood abuse, family conflicts, social adversity, and psychopathology. While suicide risk is also known to be heritable, few studies have investigated genetic risk in younger individuals.
Methods
Using polygenic risk score analysis, we examined whether genetic susceptibility to major psychiatric disorders is associated with suicidal behaviors among 11,878 children enrolled in the ABCD (Adolescent Brain Cognitive Development) Study. Suicidal ideation and suicide attempt data were assessed using the youth report of the Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5. After performing robust quality control of genotype data, unrelated individuals of European descent were included in analyses (n = 4344).
Results
Among 8 psychiatric disorders we examined, depression polygenic risk scores were associated with lifetime suicide attempts both in the baseline (odds ratio = 1.55, 95% CI = 1.10–2.18, p = 1.27 × 10−2) and in the follow-up year (odds ratio = 1.38, 95% CI = 1.08–1.77, p = 1.05 × 10−2), after adjusting for children’s age, sex, socioeconomic backgrounds, family history of suicide, and psychopathology. In contrast, attention-deficit/hyperactivity disorder polygenic risk scores were associated with lifetime suicidal ideation (odds ratio = 1.15, 95% CI = 1.05–1.26, p = 3.71 × 10−3), suggesting a distinct contribution of the genetic risk underlying attention-deficit/hyperactivity disorder and depression on suicidal behaviors of children.
Conclusions
The largest genetic sample of suicide risk data in U.S. children suggests a significant genetic basis of suicide risk related to attention-deficit/hyperactivity disorder and depression. Further research is warranted to examine whether incorporation of genomic risk may facilitate more targeted screening and intervention efforts.
Keywords: ADHD; Adolescents; Children; Depression; Polygenic risk score; Suicide
The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk. Specifically, we define the tree induced error (TIE) on a hierarchical semantic tree and extend it to its increasing function from the optimization perspective. The semantic similarity in each level of a tree is integrated with the information gain. We achieve promising results on several large scale image classification tasks with a semantic tree structure in a plug and play manner.
The goal of our project is to answer Problem 3 of Andrew Sutherland’s Elliptic Curves Problem Set 4. That is, we want to determine the probability that a random elliptic curve defined over a finite field Fp has an Fp point of prime order l, where p is either a fixed prime much larger than l, or a prime varying over some large interval. In order to do so, we must review some key concepts and theorems to gain a thorough understanding of elliptic curves. In the first part of this report, we will lead you through these key concepts and present a summary of the background material we studied throughout the course of our REU. In the second part, we will guide you through a problem on the probability of l-torsion and share our findings. Our methods included deriving combinatorial formulas to describe probabilities, as well as writing Sage scripts to verify our results..