Introduction Genome-wide association studies (GWAS) on a wide range of important human traits have identified hundreds of variants in multi-cohort meta-analyses that have highly significant associations (p<5 x10-8) that replicate across studies. However, the identified genetic variants, or single nucleotide polymorphisms (SNPs), generally explain a very small fraction of variability in the trait of interest. Moreover, for most behavioral and health scientists, the use of such massive and complex genetic data is unwieldy. In response to the desire to harness genome-wide information, use of polygenic scores (PGSs), also known as "genetic risk scores", has grown rapidly in the social, behavioral, and health sciences recently (see (1)). PGSs use the results of GWAS-typically in the form of effect estimates or p-values for each nucleotide locus-to summarize an individual's genetic association with a given trait. They can provide a single summary of measured genetic contribution of a trait that is easily integrated into more mainstream analyses of health and behavior. PGSs have multiple uses including improving risk prediction modeling controlling for some portion of variation due to genetics, investigating the common genetic basis among diseases, and estimating the genetic susceptibility of traits not measured but of high interest in a cohort (e.g. a PGS for post-traumatic stress disorder in a study of veterans or a cardiovascular PGS in a cognition study). PGSs are approximately normally distributed and relatively easy to construct. In short they appear to provide a relatively straightforward mechanism to integrate large amounts of genetic information into studies that are too small for genetic discovery, did not collect the health outcomes of interest, or do not have experience using large-scale genetic data. The relative ease of creation and use has led to a wide range of practices in creating PGSs (2-4).These practices vary based on researchers' decisions, including: 1) using genotyped SNPs versus imputed SNPs, 2) selecting SNPs for inclusion in the PGS from a meta-analysis of GWAS studies based on a particular p-value threshold, 3) whether to account for linkage disequilibrium (LD) across the genome, and 4) the effect of different options for accounting for linkage disequilibrium. And yet, despite their rapid adoption in the social sciences, best practices for PGS construction and evaluation remains relatively unexplored. Many researchers use programs such as PLINK (5), PRSice (6), and LDPred (7), designed to make PGS creation relatively simple. For novice users, these programs have produced a 'black box' creation platform that does not explore and display the range of PGSs that could be estimated. Moreover, researchers rarely report in sufficient detail the decisions, thresholds, and options used in the construction of their scores, which is likely to affect the replicability and reliability of PGSs. The degree to which all of these decisions and the combination of these decisions influence the. CC-BY-NC-ND 4.0...

Neuroimaging has suggested that amygdala reactivity to emotional facial expressions is associated with antisocial behavior (AB), particularly among those high on callous-unemotional (CU) traits. To investigate this association and potential moderators of this relationship, including task/stimuli effects, subregional anatomy of the amygdala, and participant race, we used fMRI in a sample of 167 racially diverse, 20 year-old men from low-income families. We found that AB, but not CU traits, was negatively related to amygdala reactivity to fearful faces. This result was specific to fearful faces and strongest in the centro-medial subregion of the amygdala. Arrest record was positively related to basolateral amygdala reactivity to fearful and angry faces. Results were strongest among those identified as African American and not present in those identified as European American. Our findings suggest substantial complexity in the relationship between amygdala function and AB reflecting moderating effects of task stimulus, subregional anatomy, and race.

We examined whether maltreatment experienced in childhood and/or adolescence prospectively predicts young adult functioning in a diverse and well-characterized sample of females with childhood-diagnosed attention-deficit/hyperactivity disorder (N = 140). Participants were part of a longitudinal study and carefully evaluated in childhood, adolescence, and young adulthood (Mage = 9.6, 14.3, and 19.7 years, respectively), with high retention rates across time. A thorough review of multisource data reliably established maltreatment status for each participant (Mκ = 0.78). Thirty-two (22.9%) participants experienced at least one maltreatment type (physical abuse, sexual abuse, or neglect). Criterion variables included a broad array of young adult measures of functioning gleaned from multiple-source, multiple-informant instruments. With stringent statistical control of demographic, prenatal, and family status characteristics as well as baseline levels of the criterion variable in question, maltreated participants were significantly more impaired than nonmaltreated participants with respect to self-harm (suicide attempts), internalizing symptomatology (anxiety and depression), eating disorder symptomatology, and well-being (lower overall self-worth). Effect sizes were medium. Comprising the first longitudinal evidence linking maltreatment with key young adult life impairments among a carefully diagnosed and followed sample of females with attention-deficit/hyperactivity disorder, these findings underscore the clinical importance of trauma experiences within this population.

We describe an ecological approach to understanding the developing brain, with a focus on the effects of poverty-related adversity on brain function. We articulate how combining multilevel ecological models from developmental science and developmental psychopathology with human neuroscience can inform our approach to understanding the developmental neuroscience of risk and resilience. To illustrate this approach, we focus on associations between poverty and brain function, the roles parents and neighborhoods play in this context, and the potential impact of developmental timing. We also describe the major challenges and needed advances in these areas of research to better understand how and why poverty-related adversity may impact the developing brain, including the need for: a population neuroscience approach with greater attention to sampling and representation, genetically informed and causal designs, advances in assessing context and brain function, caution in interpretation of effects, and a focus on resilience. Work in this area has major implications for policy and prevention, which are discussed.

Background Early life adversities including harsh parenting, maternal depression, neighborhood deprivation, and low family economic resources are more prevalent in low-income urban environments and are potent predictors of psychopathology, including, for boys, antisocial behavior (AB). However, little research has examined how these stressful experiences alter later neural function. Moreover, identifying genetic markers of greater susceptibility to adversity is critical to understanding biopsychosocial pathways from early adversity to later psychopathology. Methods Within a sample of 310 low-income boys followed from age 1.5 to 20, multimethod assessments of adversities were examined at age 2 and age 12. At age 20, amygdala reactivity to emotional facial expressions was assessed using fMRI, and symptoms of Antisocial Personality Disorder were assessed via structured clinical interview. Genetic variability in cortisol signaling (CRHR1) was examined as a moderator of pathways to amygdala reactivity. Results Observed parenting and neighborhood deprivation at age 2 each uniquely predicted amygdala reactivity to emotional faces at age 20 over and above other adversities measured at multiple developmental periods. Harsher parenting and greater neighborhood deprivation in toddlerhood predicted clinically-significant symptoms of AB via less amygdala reactivity to fearful facial expressions and this pathway was moderated by genetic variation in CRHR1. Conclusions These results elucidate a pathway linking early adversity to less amygdala reactivity to social signals of interpersonal distress 18 years later, which in turn increased risk for serious AB. Moreover, these findings suggest a genetic marker of youth more susceptible to adversity.