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Kumar, G.

Publications and source records attributed to Kumar, G..

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Pupation site preference selection in Drosophila jambulina

Larvae of Drosophila jambulina belonging to montium subgroup were tested for pupation site preference in relation to temperature. At higher temperature (30 {degrees}C), larvae preferred to pupate on food whereas at lower temperature (21 {degrees}C) pupation occurred on the cotton. Genetic basis of larval pupation behavior was studied by conducting reciprocal crosses for 30 generations on food-selected and on cotton-selected larvae. Results from genetic analysis between food-selected and cotton-selected strains suggested a single gene responsible for the pupation site preference, with F1 progeny pupated on cotton and F2 (F1x;F1) larvae pupated on both food as well on cotton. Although we found no change in morphological traits in food vs. cotton selected population, significantly different growth rate (body weight) between the two strains was observed. These results suggest that pupation site preferences can affect life-history traits in D. jambulina.

animal behavior and cognition

Proof Of Concept: Molecular Prediction Of Schizophrenia Risk

Key PointsO_ST_ABSQuestionC_ST_ABSTo what extent do global polygenic risk scores (PRS), molecular pathway-specific PRS, complement component (C4) gene expression, MHC loci, sex, and ancestry jointly contribute to risk for schizophrenia-spectrum disorders (SZ)?\n\nFindingsGlobal polygenic risk for schizophrenia, sex, and their interaction most robustly predict risk in a classification and regression tree model, with highest risk groups having 50/50 chance of SZ.\n\nMeaningPsychometric risk indicators, such as prodromal symptom assessments, may be enhanced by the examination of genetic risk metrics. Preliminary results suggest that of genetic risk metrics, global polygenic information has the most potential to significantly aide in the prediction of SZ.\n\nAbstractO_ST_ABSImportanceC_ST_ABSSchizophrenia (SZ) has a complex, heterogeneous symptom presentation with limited established associations between biological markers and illness onset. Many (gene) molecular pathways (MPs) are enriched for SZ signal, but it is still unclear how these MPs, global PRS, major histocompatibility complex (MHC) complement component (C4) gene expression, and MHC loci might jointly contribute to SZ and its clinical presentation. It is also unclear whether sex or ancestry interacts with these metrics to increase risk in certain individuals.\n\nObjectiveTo examine multiple genetic metrics, sex, and their interactions as possible predictors of SZ risk. Genetic information could aid in the clinical prediction of risk, but it is still unclear which genetic metrics are most promising, and how sex interacts with genetic risk metrics.\n\nDesign, Setting, and ParticipantsTo examine molecular risk in a proof-of-concept study, we used the Wellcome Trust case-control cohort and classified cases as a function of 1) polygenic risk score (PRS) for both whole genome and for 345 implicated molecular pathways, 2) predicted C4 expression, 3) SZ-relevant MHC loci, 4) sex, and 5) ancestry.\n\nMain Outcomes and MeasuresPRSs, C4 expression, SZ-relevant MHC loci, sex, and ancestry as joint risk factors for SZ.\n\nResultsRecursive partitioning yielded 15 molecular risk classes and retained as significant psychosis classifiers only sex, genome-wide SZ polygenic risk, and one MP PRS. Sex was the most robust classifier in a stepwise regression, and there was a significant interaction of sex with SZ PRS on case status, suggesting males have a lower polygenic risk threshold. By down-sampling case proportion to 1% and 1.4% population base rates in males and females, respectively, high-risk subtypes defined by this model had roughly a 52% odds of developing SZ (individuals with SZ PRS elevated by 2.6 SDs; incidence = 51.8%).\n\nConclusions and RelevanceThis proof-of-concept suggests that global SZ PRS, sex, and their interaction are robust predictors of risk and that males have a lower PRS threshold for onset. Implications for the integration of these metrics with psychometrically-identified risk are discussed.

genomics