Fairness and bias correction in machine learning for depression prediction: results from four study populations

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models leart from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we provide practical recommendations to develop bias-aware ML models for depression risk prediction.Comment: 11 pages, 2 figure

Are some children genetically predisposed to poor sleep? A polygenic risk study in the general population

Background: Twin studies show moderate heritability of sleep traits: 40% for insomnia symptoms and 46% for sleep duration. Genome-wide association studies (GWAS) have identified genetic variants involved in insomnia and sleep duration in adults, but it is unknown whether these variants affect sleep during early development. We assessed whether polygenic risk scores for insomnia (PRS-I) and sleep duration (PRS-SD) affect sleep throughout early childhood to adolescence. Methods: We included 2,458 children of European ancestry (51% girls). Insomnia-related items of the Child Behavior Checklist were reported by mothers at child's age 1.5, 3, and 6 years. At 10–15 years, the Sleep Disturbance Scale for Children and actigraphy were assessed in a subsample (N = 975). Standardized PRS-I and PRS-SD (higher scores indicate genetic susceptibility for insomnia and longer sleep duration, respectively) were computed at multiple p-value thresholds based on largest GWAS to date. Results: Children with higher PRS-I had more insomnia-related sleep problems between 1.5 and 15 years (BPRS-I &lt; 0.001 =.09, 95% CI: 0.05; 0.14). PRS-SD was not associated with mother-reported sleep problems. A higher PRS-SD was in turn associated with longer actigraphically estimated sleep duration (BPRS-SD &lt; 5e08 =.05, 95% CI: 0.001; 0.09) and more wake after sleep onset (BPRS-SD &lt; 0.005 =.25, 95% CI: 0.04; 0.47) at 10–15 years, but these associations did not survive multiple testing correction. Conclusions: Children who are genetically predisposed to insomnia have more insomnia-like sleep problems, whereas those who are genetically predisposed to longer sleep have longer sleep duration, but are also more awake during the night in adolescence. This indicates that polygenic risk for sleep traits, based on GWAS in adults, affects sleep already in children.</p

Epigenomics of being bullied:changes in DNA methylation following bullying exposure

Bullying among children is ubiquitous and associated with pervasive mental health problems. However, little is known about the biological pathways that change after exposure to bullying. Epigenome-wide changes in DNA methylation in peripheral blood were studied from pre- to post measurement of bullying exposure, in a longitudinal study of the population-based Generation R Study and Avon Longitudinal Study of Parents and Children (combined n = 1,352). Linear mixed-model results were meta-analysed to estimate how DNA methylation changed as a function of exposure to bullying. Sensitivity analyses including co-occurring child characteristics and risks were performed, as well as a Gene Ontology analysis. A candidate follow-up was employed for CpG (cytosine-phosphate-guanine) sites annotated to 5-HTT and NR3C1. One site, cg17312179, showed small changes in DNA methylation associated to bullying exposure (b = −2.67e-03, SE = 4.97e-04, p = 7.17e-08). This site is annotated to RAB14, an oncogene related to Golgi apparatus functioning, and its methylation levels decreased for exposed but increased for non-exposed. This result was consistent across sensitivity analyses. Enriched Gene Ontology pathways for differentially methylated sites included cardiac function and neurodevelopmental processes. Top CpG sites tended to have overall low levels of DNA methylation, decreasing in exposed, increasing in non-exposed individuals. There were no gene-wide corrected findings for 5-HTT and NR3C1. This is the first study to identify changes in DNA methylation associated with bullying exposure at the epigenome-wide significance level. Consistent with other population-based studies, we do not find evidence for strong associations between bullying exposure and DNA methylation

Genome-wide DNA methylation patterns associated with sleep and mental health in children: a population-based study

Background: DNA methylation (DNAm) has been implicated in the biology of sleep. Yet, how DNAm patterns across the genome relate to different sleep outcomes, and whether these associations overlap with mental health is currently unknown. Here, we investigated associations of DNAm with sleep and mental health in a pediatric population. Methods: This cross-sectional study included 465 10-year-old children (51.3% female) from the Generation R Study. Genome-wide DNAm levels were measured using the Illumina 450K array (peripheral blood). Sleep problems were assessed from self-report and mental health outcomes from maternal questionnaires. Wrist actigraphy was used in 188 11-year-old children to calculate sleep duration and midpoint sleep. Weighted gene co-expression network analysis was used to identify highly comethylated DNAm ‘modules’, which were tested for associations with sleep and mental health outcomes. Results: We identified 64 DNAm modules, one of which associated with sleep duration after covariate and multiple testing adjustment. This module included CpG sites spanning 9 genes on chromosome 17, including MAPT – a key regulator of Tau proteins in the brain involved in neuronal function – as well as genes previously implicated in sleep duration. Follow-up analyses suggested that DNAm variation in this region is under considerable genetic control and shows strong blood–brain concordance. DNAm modules associated with sleep did not overlap with those associated with mental health. Conclusions: We identified one DNAm region associated with sleep duration, including genes previously reported by recent GWAS studies. Further research is warranted to examine the functional role of this region and its longitudinal association with sleep

An epigenome-wide association study of child appetitive traits and DNA methylation

The etiology of childhood appetitive traits is poorly understood. Early-life epigenetic processes may be involved in the developmental programming of appetite regulation in childhood. One such process is DNA methylation (DNAm), whereby a methyl group is added to a specific part of DNA, where a cytosine base is next to a guanine base, a CpG site. We meta-analyzed epigenome-wide association studies (EWASs) of cord blood DNAm and early-childhood appetitive traits. Data were from two independent cohorts: the Generation R Study (n = 1,086, Rotterdam, the Netherlands) and the Healthy Start study (n = 236, Colorado, USA). DNAm at autosomal methylation sites in cord blood was measured using the Illumina Infinium HumanMethylation450 BeadChip. Parents reported on their child's food responsiveness, emotional undereating, satiety responsiveness and food fussiness using the Children's Eating Behaviour Questionnaire at age 4–5 years. Multiple regression models were used to examine the association of DNAm (predictor) at the individual site- and regional-level (using DMRff) with each appetitive trait (outcome), adjusting for covariates. Bonferroni-correction was applied to adjust for multiple testing. There were no associations of DNAm and any appetitive trait when examining individual CpG-sites. However, when examining multiple CpGs jointly in so-called differentially methylated regions, we identified 45 associations of DNAm with food responsiveness, 7 associations of DNAm with emotional undereating, 13 associations of DNAm with satiety responsiveness, and 9 associations of DNAm with food fussiness. This study shows that DNAm in the newborn may partially explain variation in appetitive traits expressed in early childhood and provides preliminary support for early programming of child appetitive traits through DNAm. Investigating differential DNAm associated with appetitive traits could be an important first step in identifying biological pathways underlying the development of these behaviors.</p

Report on 2nd GO-GN Seminar

This document reports the status of the research for the GO-GN members attending to the second seminar. This seminar took place in Ljubljana (Slovenia) in conjunction with the OCWC Global ConferenceGlobal Open Educational Resources Graduate Networ