Panos Roussos

Prof. Dr. Panos Roussos

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Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 June 2018 in Nature Genetics
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Journal Article
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JSJeanne E. Savage
Jeanne E. Savage
PJPhilip R. Jansen
Philip R. Jansen
SSSven Stringer
Sven Stringer
KWKyoko Watanabe
Kyoko Watanabe
JBJulien Bryois
Julien Bryois
DPDanielle Posthuma
Danielle Posthuma
Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3,4,5,6,7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

26 January 2023 in Nature Genetics
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DDDitte Demontis
Ditte Demontis
GWG. Bragi Walters
G. Bragi Walters
GAGeorgios Athanasiadis
Georgios Athanasiadis
RWRaymond Walters
Raymond Walters
KTKaren Therrien
Karen Therrien
ABAnders D. Børglum
Anders D. Børglum

19 July 2024 in Science
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Journal Article
Open access
AVAnurag Verma
Anurag Verma
JHJennifer E. Huffman
Jennifer E. Huffman
ARAlex Rodriguez
Alex Rodriguez
MCMitchell Conery
Mitchell Conery
Molei LiuMolei Liu
Molei Liu
KLKatherine P. Liao
Katherine P. Liao
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third ( n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

19 March 2018 in Bioinformatics
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CGClaudia Giambartolomei
Claudia Giambartolomei
JLJimmy Zhenli Liu
Jimmy Zhenli Liu
WZWen Zhang
Wen Zhang
MHMads Hauberg
Mads Hauberg
HSHuwenbo Shi
Huwenbo Shi
Panos RoussosPanos Roussos
Panos Roussos
Most genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g. expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work, we propose multiple-trait-coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci. We applied moloc to schizophrenia (SCZ) and eQTL/mQTL data derived from human brain tissue and identified 52 candidate genes that influence SCZ through methylation. Our method can be applied to any GWAS and relevant functional data to help prioritize disease associated genes. Availability and implementation: moloc is available for download as an R package (https://github.com/clagiamba/moloc). We also developed a web site to visualize the biological findings (icahn.mssm.edu/moloc). The browser allows searches by gene, methylation probe and scenario of interest. Supplementary data are available at Bioinformatics online.

18 June 2021 in Science
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Journal Article
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BZBingxin Zhao
Bingxin Zhao
TLTengfei Li
Tengfei Li
YYYue Yang
Yue Yang
XWXifeng Wang
Xifeng Wang
TLTianyou Luo
Tianyou Luo
HZHongtu Zhu
Hongtu Zhu
Connecting the dots on white matter: The white matter of the brain, which is composed of axonal tracts connecting different brain regions, plays key roles in both normal brain function and a variety of neurological disorders. Zhao et al. combined detailed magnetic resonance imaging–based assessment of brain structures with genetic data on nearly 44,000 individuals (see the Perspective by Filley). On the basis of this comprehensive analysis, the authors identified structural and genetic abnormalities associated with neurological and psychiatric disorders, as well as some nondisease traits, thus creating a valuable resource and providing some insights into the underlying neurobiology. Science , abf3736, this issue p. eabf3736 ; see also abj1881, p. 1265

1 November 2014 in Cell Reports
Verified
Journal Article
Open access
Panos RoussosPanos Roussos
Panos Roussos
AMAmanda C. Mitchell
Amanda C. Mitchell
GVGeorgios Voloudakis
Georgios Voloudakis
JFJohn F. Fullard
John F. Fullard
VPVenu M. Pothula
Venu M. Pothula
PSPamela Sklar
Pamela Sklar
A large portion of common variant loci associated with genetic risk for schizophrenia reside within noncoding sequence of unknown function. Here, we demonstrate promoter and enhancer enrichment in schizophrenia variants associated with expression quantitative trait loci (eQTL). The enrichment is greater when functional annotations derived from the human brain are used relative to peripheral tissues. Regulatory trait concordance analysis ranked genes within schizophrenia genome-wide significant loci for a potential functional role, based on colocalization of a risk SNP, eQTL, and regulatory element sequence. We identified potential physical interactions of noncontiguous proximal and distal regulatory elements. This was verified in prefrontal cortex and -induced pluripotent stem cell-derived neurons for the L-type calcium channel (CACNA1C) risk locus. Our findings point to a functional link between schizophrenia-associated noncoding SNPs and 3D genome architecture associated with chromosomal loopings and transcriptional regulation in the brain.