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Article ; Online: Genetic Intersections of Language and Neuropsychiatric Conditions.

Koomar, Tanner / Michaelson, Jacob J

2020 Volume 22, Issue 1, Page(s) 4

Abstract: Purpose of review: To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes ... ...

Abstract	Purpose of review: To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes implicated in both language and neuropsychiatric disorders, as they reveal biological domains likely to underpin the processes controlling both. Recent findings: Abnormal language and communication are common manifestations of neuropsychiatric conditions, and children with impaired language are more likely to develop psychiatric disorders than their peers. Major themes in the genetics of both language and psychiatry include master transcriptional regulators, like FOXP2; key developmental regulators, like AUTS2; and mediators of neurotransmission, like GRIN2A and CACNA1C.
MeSH term(s)	Communication ; Humans ; Language ; Mental Disorders/genetics
Language	English
Publishing date	2020-01-17
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2055376-6
ISSN	1535-1645 ; 1523-3812
ISSN (online)	1535-1645
ISSN	1523-3812
DOI	10.1007/s11920-019-1123-z
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Article: Lingo: an automated, web-based deep phenotyping platform for language ability.

Casten, Lucas G / Koomar, Tanner / Elsadany, Muhammad / McKone, Caleb / Tysseling, Ben / Sasidharan, Mahesh / Tomblin, J Bruce / Michaelson, Jacob J

medRxiv : the preprint server for health sciences

2024

Abstract: Background: Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit.: Methods: Here, we ... ...

Abstract	Background: Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Methods: Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. Results: We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Conclusions: Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.
Language	English
Publishing date	2024-03-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.29.24305034
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Article ; Online: Author Correction: Forecasting risk gene discovery in autism with machine learning and genome-scale data.

Brueggeman, Leo / Koomar, Tanner / Michaelson, Jacob J

Scientific reports

2020 Volume 10, Issue 1, Page(s) 20994

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

Abstract	An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Language	English
Publishing date	2020-11-26
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-77832-2
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Article ; Online: Forecasting risk gene discovery in autism with machine learning and genome-scale data.

Brueggeman, Leo / Koomar, Tanner / Michaelson, Jacob J

Scientific reports

2020 Volume 10, Issue 1, Page(s) 4569

Abstract: Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have ... ...

Abstract	Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true "autism risk genes". Massive genetic studies are currently underway producing data to implicate additional genes. This approach - although necessary - is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene's involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene's involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.
MeSH term(s)	Autism Spectrum Disorder/genetics ; Cluster Analysis ; Early Diagnosis ; Gene Regulatory Networks ; Genetic Markers ; Genetic Predisposition to Disease/genetics ; Genomics/methods ; Humans ; Machine Learning ; Sequence Analysis, DNA
Chemical Substances	Genetic Markers
Language	English
Publishing date	2020-03-12
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-61288-5
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Article ; Online: Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population.

Thomas, Taylor R / Koomar, Tanner / Casten, Lucas G / Tener, Ashton J / Bahl, Ethan / Michaelson, Jacob J

Translational psychiatry

2022 Volume 12, Issue 1, Page(s) 247

Abstract: The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with ... ...

Abstract	The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with other psychiatric and behavior traits in the same manner as autism case-control status. In N = 6064 autistic children in the SPARK cohort, we investigated the common genetic properties of twelve subscales from three clinical autism instruments measuring autistic traits: the Social Communication Questionnaire (SCQ), the Repetitive Behavior Scale-Revised (RBS-R), and the Developmental Coordination Disorder Questionnaire (DCDQ). Educational attainment polygenic scores (PGS) were significantly negatively correlated with eleven subscales, while ADHD and major depression PGS were positively correlated with ten and eight of the autism subscales, respectively. Loneliness and neuroticism PGS were also positively correlated with many subscales. Significant PGS by sex interactions were found-surprisingly, the autism case-control PGS was negatively correlated in females and had no strong correlation in males. SNP-heritability of the DCDQ subscales ranged from 0.04 to 0.08, RBS-R subscales ranged from 0.09 to 0.24, and SCQ subscales ranged from 0 to 0.12. GWAS in SPARK followed by estimation of polygenic scores (PGS) in the typically-developing ABCD cohort (N = 5285), revealed significant associations of RBS-R subscale PGS with autism-related behavioral traits, with several subscale PGS more strongly correlated than the autism case-control PGS. Overall, our analyses suggest that the clinical autism subscale traits show variability in SNP-heritability, PGS associations, and significant PGS by sex interactions, underscoring the heterogeneity in autistic traits at a genetic level. Furthermore, of the three instruments investigated, the RBS-R shows the greatest evidence of genetic signal in both (1) autistic samples (greater heritability) and (2) general population samples (strongest PGS associations).
MeSH term(s)	Autism Spectrum Disorder/psychology ; Autistic Disorder/epidemiology ; Autistic Disorder/genetics ; Child ; Communication ; Female ; Humans ; Male ; Multifactorial Inheritance ; Phenotype
Language	English
Publishing date	2022-06-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-022-01982-2
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Article: Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort.

Koomar, Tanner / Thomas, Taylor R / Pottschmidt, Natalie R / Lutter, Michael / Michaelson, Jacob J

Frontiers in psychiatry

2021 Volume 12, Page(s) 668297

Abstract: This study is the first genetically-informed investigation of avoidant/restrictive food intake disorder (ARFID), an eating disorder that profoundly impacts quality of life for those affected. ARFID is highly comorbid with autism, and we provide the first ...

Abstract	This study is the first genetically-informed investigation of avoidant/restrictive food intake disorder (ARFID), an eating disorder that profoundly impacts quality of life for those affected. ARFID is highly comorbid with autism, and we provide the first estimate of its prevalence in a large and phenotypically diverse autism cohort (a subsample of the SPARK study,
Language	English
Publishing date	2021-06-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2021.668297
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Article: cerebroViz: an R package for anatomical visualization of spatiotemporal brain data

Bahl, Ethan / Koomar, Tanner / Michaelson, Jacob J

Bioinformatics. 2017 Mar. 01, v. 33, no. 5

2017

Abstract: Summary: Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease’s age at onset and ...

Abstract	Summary: Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease’s age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene–disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics. Availability and Implementation: cerebroViz is freely available through GitHub (https://github.com/ethanbahl/cerebroViz). The tutorial is available at http://ethanbahl.github.io/cerebroViz/ Contacts: ethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords	adulthood ; bioinformatics ; brain ; computer software ; databases ; epigenetics ; fetal development ; gene expression ; humans ; transcriptomics
Language	English
Dates of publication	2017-0301
Size	p. 762-763.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw726
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: cerebroViz: an R package for anatomical visualization of spatiotemporal brain data.

Bahl, Ethan / Koomar, Tanner / Michaelson, Jacob J

Bioinformatics (Oxford, England)

2016 Volume 33, Issue 5, Page(s) 762–763

Abstract	Summary: Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease's age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics. Availability and implementation: cerebroViz is freely available through GitHub ( https://github.com/ethanbahl/cerebroViz ). The tutorial is available at http://ethanbahl.github.io/cerebroViz/. Contacts: ethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Brain/anatomy & histology ; Brain/growth & development ; Brain/metabolism ; Databases, Factual ; Gene Expression ; Gene Expression Profiling/methods ; Humans ; Software ; Spatio-Temporal Analysis
Language	English
Publishing date	2016-12-23
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw726
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Article ; Online: Whole-genome sequencing in a family with twin boys with autism and intellectual disability suggests multimodal polygenic risk.

McKenna, Brooke / Koomar, Tanner / Vervier, Kevin / Kremsreiter, Jamie / Michaelson, Jacob J

Cold Spring Harbor molecular case studies

2018 Volume 4, Issue 6

Abstract: Over the past decade, a focus on de novo mutations has rapidly accelerated gene discovery in autism spectrum disorder (ASD), intellectual disability (ID), and other neurodevelopmental disorders (NDDs). However, recent studies suggest that only a minority ...

Abstract	Over the past decade, a focus on de novo mutations has rapidly accelerated gene discovery in autism spectrum disorder (ASD), intellectual disability (ID), and other neurodevelopmental disorders (NDDs). However, recent studies suggest that only a minority of cases are attributable to de novo mutations, and instead these disorders often result from an accumulation of various forms of genetic risk. Consequently, we adopted an inclusive approach to investigate the genetic risk contributing to a case of male monozygotic twins with ASD and ID. At the time of the study, the probands were 7 yr old and largely nonverbal. Medical records indicated a history of motor delays, sleep difficulties, and significant cognitive deficits. Through whole-genome sequencing of the probands and their parents, we uncovered elevated common polygenic risk, a coding de novo point mutation in
MeSH term(s)	Adult ; Ankyrins/genetics ; Autism Spectrum Disorder/genetics ; Autistic Disorder/genetics ; Child ; Chromosomal Proteins, Non-Histone/genetics ; Family ; Female ; Genes, X-Linked ; Genetic Predisposition to Disease/genetics ; Humans ; Intellectual Disability/genetics ; Male ; Multifactorial Inheritance/genetics ; Mutation ; Nerve Tissue Proteins/genetics ; Neurodevelopmental Disorders/genetics ; Phenotype ; Risk Factors ; Twins, Monozygotic ; Whole Genome Sequencing/methods
Chemical Substances	ANK3 protein, human ; Ankyrins ; Chromosomal Proteins, Non-Histone ; Nerve Tissue Proteins ; centromere protein E ; neurexin IIIalpha
Language	English
Publishing date	2018-12-17
Publishing country	United States
Document type	Case Reports ; Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2835759-0
ISSN	2373-2873 ; 2373-2873
ISSN (online)	2373-2873
ISSN	2373-2873
DOI	10.1101/mcs.a003285
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Article ; Online: Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts.

McKenna, Brooke G / Huang, Yongchao / Vervier, Kévin / Hofammann, Dabney / Cafferata, Mary / Al-Momani, Seima / Lowenthal, Florencia / Zhang, Angela / Koh, Jin-Young / Thenuwara, Savantha / Brueggeman, Leo / Bahl, Ethan / Koomar, Tanner / Pottschmidt, Natalie / Kalmus, Taylor / Casten, Lucas / Thomas, Taylor R / Michaelson, Jacob J

Molecular autism

2021 Volume 12, Issue 1, Page(s) 43

Abstract: Background: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought ... ...

Abstract	Background: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed, and the relationships between androgen exposure and behavior remain unclear. Methods: Here, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N = 763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores. Results: We found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all [Formula: see text]), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ([Formula: see text], [Formula: see text]; [Formula: see text], [Formula: see text], respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels ([Formula: see text], [Formula: see text]), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels ([Formula: see text], [Formula: see text]). Finally, using the SPARK cohort (N = 9419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning ([Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text] for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: [Formula: see text]; testosterone: [Formula: see text] from 0.1%-ile to 99.9%-ile; SHBG: [Formula: see text] from 0.1%-ile to 99.9%-ile). Limitations: In the devGenes and SPARK cohorts, our analyses rely on indirect, rather than direct measurement of androgens and related molecules. Conclusions: These findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females.
MeSH term(s)	Androgens ; Autism Spectrum Disorder ; Cohort Studies ; Female ; Humans ; Male ; Multifactorial Inheritance ; Testosterone
Chemical Substances	Androgens ; Testosterone (3XMK78S47O)
Language	English
Publishing date	2021-06-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2540930-X
ISSN	2040-2392 ; 2040-2392
ISSN (online)	2040-2392
ISSN	2040-2392
DOI	10.1186/s13229-021-00450-w
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