Ferris, E. | Search bioRxiv

bioRxiv2018

Machine Learning Reveals Modules of Economic Behavior from Foraging Mice

Complex ethological behaviors could be constructed from modules that are discrete functional units of behavior with a genetic basis. Here, we test this idea for foraging, and develop a paradigm to dissect foraging patterns in mice. We uncover discrete behavioral modules linked to round trip excursions from the home. Machine learning reveals 59 modules across different genetic backgrounds and ages. Different modules develop at different ages and are linked to different aspects of economic behavior, including memory, reward, risk and effort responses. Crosses of distant mouse strains reveal that parental and genetic effects shape foraging differently, and parental effects grow stronger with age. Specific behavioral modules, genes and pathways are found to be sensitive to parental effects. One candidate gene, Magel2, is linked to Prader-Willi Syndrome and shaped the expression of discrete modules in an age-dependent manner. Our results reveal building blocks for normal and abnormal economic behavior patterns.\n\nHIGHLIGHTSO_LIIdentification of 59 economic behavior modules underlying foraging\nC_LIO_LIDiscrete modules are linked to memory, reward, risk and effort responses\nC_LIO_LIGenetic and parental effects shape foraging by changing module expression\nC_LIO_LIMagel2, a Prader-Willi Syndrome gene, affects specific modules at specific ages\nC_LI

neuroscience↗

bioRxiv2017

Identification of genome-wide significant shared genomic segments in large extended Utah families at high risk for completed suicide

Suicide is the 10th leading cause of death in the US. While environment has undeniable impact, evidence suggests genetic factors play a significant role in completed suicide. We linked a resource of >4,500 DNA samples from completed suicides obtained from the Utah Medical Examiner to genealogical records and medical records data available on over 8 million individuals. This linking has resulted in the identification of high-risk extended families (7-9 generations) with significant familial risk of completed suicide. Familial aggregation across distant relatives minimizes effects of shared environment, provides more genetically homogeneous risk groups, and magnifies genetic risks through familial repetition. We analyzed Illumina PsychArray genotypes from suicide cases in 43 high-risk families, identifying 30 distinct shared genomic segments with genome-wide evidence (p=2.02E-07 to 1.30E-18) of segregation with completed suicide. The 207 genes implicated by the shared regions provide a focused set of genes for further study; 18 have been previously associated with suicide risk. While PsychArray variants do not represent exhaustive variation within the 207 genes, we investigated these for specific segregation within the high-risk families, and for association of variants with predicted functional impact in ~1300 additional Utah suicides unrelated to the discovery families. None of the limited PsychArray variants explained the high-risk family segregation; sequencing of these regions will be needed to discover segregating risk variants, which may be rarer or regulatory. However, additional association tests yielded four significant PsychArray variants (SP110, rs181058279; AGBL2, rs76215382; SUCLA2, rs121908538; APH1B, rs745918508), raising the likelihood that these genes confer risk of completed suicide.

genetics↗