Browsing by Author "Merikangas, Alison"

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    Convergence of genes and cellular pathways dysregulated in autism spectrum disorders
    (Elsevier, 2014-03-25) Pinto, Dalila; Delaby, Elsa; Merico, Daniele; Barbosa, Mafalda; Merikangas, Alison; Klei, Lambertus; Thiruvahindrapuram, Bhooma; Xu, Xiao; Ziman, Robert; Wang, Zhuozhi; Vorstman, Jacob A.S.; Thompson, Ann; Regan, Regina; Pilorge, Marion; Pellecchia, Giovanna; Pagnamenta, Alistair T.; Oliveira, Barbara; Marshall, Christian R.; Magalhaes, Tiago R.; Lowe, Jennifer K.; Howe, Jennifer L.; Griswold, Anthony J.; Gilbert, John; Duketis, Eftichia; Dombroski, Beth A.; De Jonge, Maretha V.; Cuccaro, Michael; Crawford, Emily L.; Correia, Catarina T.; Conroy, Judith; Conceicao, Ines C.; Chiocchetti, Andreas G.; Casey, Jillian P.; Cai, Guiqing; Cabrol, Christelle; Bolshakova, Nadia; Bacchelli, Elena; Anney, Richard; Gallinger, Steven; Cotterchio, Michelle; Casey, Graham; Zwaigenbaum, Lonnie; Wittemeyer, Kerstin; Wing, Kirsty; Wallace, Simon; van Engeland, Herman; Tryfon, Ana; Thomson, Susanne; Soorya, Latha; Roge´, Bernadette; Roberts, Wendy; Poustka, Fritz; Mouga, Susana; Minshew, Nancy; McInnes, L. Alison; McGrew, Susan G.; Lord, Catherine; Leboyer, Marion; Le Couteur, Ann S.; Kolevzon, Alexander; Gonzalez, Patricia Jimenez; Jacob, Suma; Holt, Richard; Guter, Stephen; Green, Jonathan; Green, Andrew; Gillberg, Christopher; Fernandez, Bridget A.; Duque, Frederico; Delorme, Richard; Dawson, Geraldine; Chaste, Pauline; Cafe, Catia; Brennan, Sean; Bourgeron, Thomas; Thomas, Patrick F.; Bolte, Sven; Bernier, Raphael; Baird, Gillian; Bailey, Anthony J.; Anthony J., Evdokia; Almeida, Joana; Wijsman, Ellen M.; Vieland, Veronica J.; Vicente, Astrid M.; Schellenberg, Gerard D.; Pericak-Vance, Margaret; Paterson, Andrew D.; Parr, Jeremy R.; Oliveira, Guiomar; Nurnberger, John I.; Monaco, Anthony P.; Maestrini, Elena; Klauck, Sabine M.; Hakonarson, Hakon; Haines, Jonathan L.; Geschwind, Daniel H.; Freitag, Christine M.; Folstein, Susan E.; Ennis, Sean; Coon, Hilary; Battaglia, Agatino; Szatmari, Peter; Sutcliffe, James S.; Hallmayer, Joachim; Gill, Michael; Cook, Edwin H.; Buxbaum, Joseph D.; Devlin, Bernie; Gallagher, Louise; Betancur, Catalina; Scherer, Stephen W.; Psychiatry, School of Medicine
    Rare copy-number variation (CNV) is an important source of risk for autism spectrum disorders (ASDs). We analyzed 2,446 ASD-affected families and confirmed an excess of genic deletions and duplications in affected versus control groups (1.41-fold, p = 1.0 × 10−5) and an increase in affected subjects carrying exonic pathogenic CNVs overlapping known loci associated with dominant or X-linked ASD and intellectual disability (odds ratio = 12.62, p = 2.7 × 10−15, ∼3% of ASD subjects). Pathogenic CNVs, often showing variable expressivity, included rare de novo and inherited events at 36 loci, implicating ASD-associated genes (CHD2, HDAC4, and GDI1) previously linked to other neurodevelopmental disorders, as well as other genes such as SETD5, MIR137, and HDAC9. Consistent with hypothesized gender-specific modulators, females with ASD were more likely to have highly penetrant CNVs (p = 0.017) and were also overrepresented among subjects with fragile X syndrome protein targets (p = 0.02). Genes affected by de novo CNVs and/or loss-of-function single-nucleotide variants converged on networks related to neuronal signaling and development, synapse function, and chromatin regulation.
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    Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features
    (MDPI, 2023-05-18) Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Meyers, Jacquelyn L.; Kinreich, Sivan; Pandey, Gayathri; Subbie-Saenz de Viteri, Stacey; Zhang, Jian; Kuang, Weipeng; Barr, Peter B.; Aliev, Fazil; Anokhin, Andrey P.; Plawecki, Martin H.; Kuperman, Samuel; Almasy, Laura; Merikangas, Alison; Brislin, Sarah J.; Bauer, Lance; Hesselbrock, Victor; Chan, Grace; Kramer, John; Lai, Dongbing; Hartz, Sarah; Bierut, Laura J.; McCutcheon, Vivia V.; Bucholz, Kathleen K.; Dick, Danielle M.; Schuckit, Marc A.; Edenberg, Howard J.; Porjesz, Bernice; Psychiatry, School of Medicine
    Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50–81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive “uplift” life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.