Biology subjects

Jahagirdar, S.

Publications and source records attributed to Jahagirdar, S..

2 recordsLinked to original sources

bioRxiv2025

Genome-wide analysis of social behaviour in context: a meta-regression approach across social domains, reporters and developmental stages

Social behaviour is a heritable, context-dependent trait that changes across social settings and development, influencing wellbeing and mental health. We present the first genome-wide meta-regression study of social behaviour from infancy to early adulthood, leveraging 491,246 repeat measures of low prosocial behaviour and peer/social difficulties in European-ancestry cohorts (Neff=121,777, Nind=73,321). We modelled heterogeneity in genetic effects across social domains, informants, and ages (2-29 years), capturing social context through genomic influences. Six loci were identified, including variation within CADM2 (p=2.51x10-9). The SNP-based heritability was modest (2-7%), and the genetic architecture of social behaviour multidimensional. Polygenic scores demonstrated predictability and accuracy in independent European-ancestry cohorts and, partially, in African-ancestry cohorts (Nind=16,305). Genetic correlations with later-life and mental health outcomes showed context-dependent patterns. Modelling predicted onsets of associations with social behaviour revealed distinct profiles, as observed for autism, ADHD, depression and schizophrenia, highlighting novel opportunities to genetically proxy developmental trajectories.

genomics↗

bioRxiv2018

Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms

Biological networks play a paramount role in our understanding of complex biological phenomena and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS and modifications of the MR-NET algorithm, together with standard Pearsons and Spearmans correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample size and noise levels. We found that inference algorithms based on resampling and bootstrapping to perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns which are more similar to those observed in real metabolomics data.

systems biology↗