Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

TY - JOUR

T1 - Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

AU - Spargo, Thomas P

AU - Gilchrist, Lachlan

AU - Hunt, Guy P

AU - Dobson, Richard J B

AU - Proitsi, Petroula

AU - Al-Chalabi, Ammar

AU - Pain, Oliver

AU - Iacoangeli, Alfredo

N1 - © 2023, Spargo et al.

PY - 2024/12/17

Y1 - 2024/12/17

N2 - Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant 'local' genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson's disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

AB - Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant 'local' genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson's disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

KW - Humans

KW - Genome-Wide Association Study

KW - Genetic Predisposition to Disease

KW - Mental Disorders/genetics

KW - Amyotrophic Lateral Sclerosis/genetics

KW - Schizophrenia/genetics

KW - Genetic Loci

KW - Parkinson Disease/genetics

KW - Alzheimer Disease/genetics

U2 - 10.7554/eLife.88768

DO - 10.7554/eLife.88768

M3 - Article

C2 - 39688956

SN - 2050-084X

VL - 12

JO - eLife

JF - eLife

M1 - 88768

ER -