Genetics of Neonatal Lupus Erythematosus Risk and Specific Manifestations.

OBJECTIVE: Neonatal lupus erythematosus (NLE) is a passively acquired autoimmune disease of infants born to anti-Ro and/or La autoantibody positive mothers. Genetics may impact NLE risk. We analyzed the genetics of infants and anti-Ro antibody positive mothers, with NLE and NLE specific manifestations. METHODS: Infants and mothers from a tertiary care clinic underwent genotyping on the Global Screening Array. We created additive non-HLA and HLA polygenic risk scores (PRSs) for systemic lupus erythematosus (SLE), from one of the largest genome wide association studies. Outcomes were any NLE manifestations, cardiac NLE, and cutaneous NLE. We tested the association between SLE-PRSs in the infant, mother, and the PRS difference between the mother and infant with NLE outcomes, in logistic regression and generalized linear mixed models (Bonferroni P<0.02). We also performed HLA-wide analyses for the outcomes (P<5.00x10-8). RESULTS: The study included 332 infants, 270 anti-Ro antibody positive mothers, and 253 mother-infant pairs. A large proportion of mothers (40.3%) and infants (41.3%) were European, and 50.0% of infants were female. More than half of the infants had NLE (53.0%), including 7.2% with cardiac NLE and 11.7% with cutaneous NLE. We did not identify significant associations between infant, maternal, or maternal-infant PRSs and any NLE outcomes. HLA-wide analyses did not identify NLE risk alleles. CONCLUSION: In a multiethnic cohort of infants and anti-Ro antibody positive mothers, we did not identify a significant association between SLE genetics and risk of NLE outcomes.

Interactions between genetic risk for 21 neurodevelopmental and psychiatric disorders and sport activity on youth mental health.

Childhood is a sensitive period where behavioral disturbances, determined by genetics and environmental factors including sport activity, may emerge and impact risk of mental illness in adulthood. We aimed to determine if participation in sports can mitigate genetic risk for neurodevelopmental and psychiatric disorders in youth. We analyzed 4975 unrelated European youth (ages 9-10) from the Adolescent Brain Cognitive Development Study. Our outcomes were eight Child Behavior Checklist (CBCL) scores, measured annually. Polygenic risk scores (PRSs) were calculated for 21 disorders, and sport frequency and type were summarized. PRSs and sport variables were tested for main effects and interactions against CBCL outcomes using linear models. Cross-sectionally, PRSs for attention-deficit/hyperactivity disorder and major depressive disorder were associated with increases in multiple CBCL outcomes. Participation in non-contact or team sports, as well as more frequent sport participation reduced all cross-sectional CBCL outcomes, whereas involvement in contact sports increased attention problems and rule-breaking behavior. Interactions revealed that more frequent exercise was significantly associated with less rule breaking behavior in individuals with high genetic risk for obsessive compulsive disorder. Associations with longitudinal CBCL outcomes demonstrated weaker effects. We highlight the importance of genetic context when considering sports as an intervention for early life behavioural problems.

Genome-Wide Sequencing Identified Rare Genetic Variants for Childhood-Onset Monogenic Lupus.

OBJECTIVE: Genetics play an important role in systemic lupus erythematosus (SLE) pathogenesis. We calculated the prevalence of rare variants in known monogenic lupus genes among children suspected of monogenic lupus. METHODS: We completed paired-end genome-wide sequencing (whole genome sequencing [WGS] or whole exome sequencing) in patients suspected of monogenic lupus, and focused on 36 monogenic lupus genes. We prioritized rare (minor allele frequency < 1%) exonic, nonsynonymous, and splice variants with predicted pathogenicity classified as deleterious variants (Combined Annotation Dependent Depletion [CADD], PolyPhen2, and Sorting Intolerant From Tolerant [SIFT] scores). Additional filtering restricted to predicted damaging variants by considering reported zygosity. In those with WGS (n = 69), we examined copy number variants (CNVs) > 1 kb in size. We created additive non-HLA and HLA SLE genetic risk scores (GRSs) using common SLE-risk single-nucleotide polymorphisms. We tested the relationship between SLE GRSs and the number of rare variants with multivariate logistic models, adjusted for sex, ancestry, and age of diagnosis. RESULTS: The cohort included 71 patients, 80% female, with a mean age at diagnosis of 8.9 (SD 3.2) years. We identified predicted damaging variants in 9 (13%) patients who were significantly younger at diagnosis compared to those without a predicted damaging variant (6.8 [SD 2.1] years vs 9.2 [SD 3.2] years, P = 0.01). We did not identify damaging CNVs. There was no significant association between non-HLA or HLA SLE GRSs and the odds of carrying ≥ 1 rare variant in multivariate analyses. CONCLUSION: In a cohort of patients with suspected monogenic lupus who underwent genome-wide sequencing, 13% carried rare predicted damaging variants for monogenic lupus. Additional studies are needed to validate our findings.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.

Background: Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide. Methods: We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide. Results: Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance. Conclusion: Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.