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The white matter of the brain is composed of bundles of axons that connect different areas of the brain, and plays a key role in brain function and various neurological diseases
.
Although differences in white matter in the general population are heritable, common genetic variations that affect the microstructure of white matter are rarely found
.
An article titled "Common genetic variation influencing human white matter microstructure" (IF: 47.
72) published in Science on June 18, 2021 correlated the genome-wide diffusion magnetic resonance imaging (dMRI) data of 43,802 individuals Research (GWAS), reveals the genetic relationship between white matter microstructure changes and various clinical diseases (such as stroke, depression, schizophrenia, etc.
), and provides new research ideas for neuropharmacology
.
Research background White matter occupies about half of the brain's volume, and a complex structural connection network is established in the brain, which occupies an important position in the transmission of neural information
.
Neuroscience evidence shows that differences in brain white matter connections are related to a variety of neuronuclear psychiatric diseases, such as attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), schizophrenia, and Alzheimer's disease
.
At present, white matter microstructure differences and abnormalities can be obtained from dMRI data.
Commonly used indicators include FA (fractional anisotropy), MD (average diffusivity), AD (axial diffusivity), RD (radial diffusivity), MO (Anisotropic Mode)
.
Both family and population-based studies have reported that DTI data of white matter microstructure has high heritability
.
GWAS is used to identify genomes related to the variation of white matter microstructure between individuals, but these studies have two main limitations: 1) The sample size is small, the largest dMRI phenotype GWAS sample size previously published is 17,706 people, a large sample The quantity is essential to improve the genetic variation of white matter with weak effects and reveal the common genetic influence with other complex traits; 2).
Spatial specificity
.
Because the differences and changes in the white matter microstructure may not have a consistent pattern throughout the pathway, GWAS studies based on the whole brain may lose information
.
The genetic influence shared between the white matter of the brain and other complex features is unknown, and explaining these underlying genetic connections may identify important brain regions that are related to clinical disease
.
Summary of research methods Data sources: UK Biobank (UKB), Adolescent Brain Cognitive Development (ABCD), HCP, Pediatric Imaging, Neurocognition and Genetics (PING), Philadelphia Neurodevelopmental Cohort Analysis (PNC)
.
Using ENIGMA-DTI imaging to obtain voxel digital ground imaging images of 43802 subjects, focusing on 21 pre-defined white matter tracts, analyzing 5 main DTI measurements, and generating 215 DTI phenotypes for each individual
.
Then, GWSA analysis was performed on 215 phenotypes to discover the genetic structure of the white matter of the brain and explore the genetic connection with a large number of clinical endpoints in different characteristic areas
.
Research results (1) Figure 1 shows the heritability of SNP (Single Nucleotide Polymorphism) for 215 DTI parameters, including 110 mean values and 105 functional blocks, using UKB phase 1 and phase 2 data, respectively, and the two groups of inheritance The correlation between force estimates is 0.
92, and heritability analysis shows that the main microstructural variations of voxel FA maps collected by unconventional specific fiber bundle cells are controlled by genetics
.
Figure 1 SNP heritability of 215 DTI parameters, including 110 mean parameters and 105 functional blocks, as well as the genomic region that affects DTI parameters
.
Each signal point indicates that at least one of the 10 DTI parameters in the region is related to the genomic region (2) Figures 2 and 3 show the gene loci related to DTI parameters and brain diseases
.
Glioma risk variants are associated with multiple white matter tracts, including the corpus callosum, internal capsule, upper longitudinal tract, and anterior radiation crown
.
In gliomas, reduced white matter microstructure integrity and structural connectivity changes are observed
.
10q24.
33, 9p21.
3, and 2q33.
2 are co-localized with stroke and are associated with cardiovascular disease, type 2 diabetes, obesity, and hypertension, indicating that the genetic relationship between white matter microstructure and stroke may be partly through vascular risk factors Mediated
.
17q21.
31, 6p22.
1 and 6p22.
2y co-localize with many risk variants of brain diseases and cognition, such as schizophrenia, Alzheimer's disease, cortical basal degeneration, Parkinson's disease, depression, alcohol use disorders, mental and cognitive ability
.
In general, genetic links have been found between the microstructure of the white matter of the brain and a wide range of clinical outcomes and complex features
.
Combining white matter GWAS with these clinical results may help us understand the underlying mechanisms leading to these results
.
Figure 2 DTI parameters and brain disease-related colocalized gene loci Figure B.
shows the genetic effects of five colocalized glioma GWAS index variants (rs634537, rs2235573, rs55705857, rs3751667, rs723527) on FA
.
D.
Shows the voxel-based genetic effects of three co-localized stroke GWAS index variants (rs2295786, rs7859727, rs18818651) on macular degeneration Figure 3 Paired genetic correlations between DTI parameters of the white matter tracts and brain diseases and cognitive functions Sex
.
The position of the white matter tract and DTI parameters are related to stroke, MDD, intelligence and reaction time (D, E) (3) In addition, the author found that 14 white matter-related genes are targets of 79 drugs, of which 23 are antipsychotic drugs, 38 One is antidepressant drugs, eight are used to treat addiction disorders, six are anti-Parkinson's disease drugs, and four are anti-dementia drugs
.
In the area enrichment analysis of the 70 complex traits studied in the genetic correlation analysis, it was found that the heritability of 49 complex traits was significantly increased, mainly concentrated in the areas affecting the microstructure of the white matter of the brain (Figure 4)
.
Common mutations related to white matter microstructure change the function of regulatory elements in glial cells (especially oligodendrocytes), indicating that genes with higher transcription levels in brain tissue also have a stronger genetic association with DTI parameters
.
Figure 4 The full text summary of the enrichment analysis of brain cell types by partition: In summary, in this study, the author collected magnetic resonance imaging data of 43,805 subjects from 5 public data sources, and analyzed the genetic structure of brain white matter
.
Finally, 109 newly discovered genomic regions with white matter microstructure differences were identified, and many brain diseases and cognitive dysfunctions were found to be related to the genetic characteristics of white matter.
These results can help clinicians and basic researchers in the onset of brain diseases.
In-depth understanding of the mechanism
.
This article shows the value of large-scale neuroimaging data integration and the application of specific FPCA in the study of human brain genetics.
Identifying the cross-group and group-specific components of human brain genetic factors may be a hot research topic in the future
.
In addition, the authors found that some commonly used centrally acting drugs have effects on white matter microstructure-related genes, indicating that the neuropharmacology of brain diseases may benefit from the relationship between these drugs and white matter, which may bring new ideas to neuropharmacology research From a perspective, it is expected to improve the efficacy of drugs
.
(Link to full text: DOI: 10.
1126/science.
abf3736) Contact for questions related to Shengxin: 18501230653 (same number on WeChat) Welcome to follow Shengxin human transcriptome | methylation | resequencing | single cell | m6A | multiomics cytoscape | limma | WGCNA | Water Bear Legend | Linux Electrophoresis | PCR | A Brief History of Sequencing | Karyotype | NIPT | Basic Experimental Genes | 2019-nCoV | Enrichment Analysis | Joint Analysis | Microenvironmental Plague Pursuit | Summary of Ideas | Scholars | Scientific Research | Retraction | PhD Reading | Work
.
Although differences in white matter in the general population are heritable, common genetic variations that affect the microstructure of white matter are rarely found
.
An article titled "Common genetic variation influencing human white matter microstructure" (IF: 47.
72) published in Science on June 18, 2021 correlated the genome-wide diffusion magnetic resonance imaging (dMRI) data of 43,802 individuals Research (GWAS), reveals the genetic relationship between white matter microstructure changes and various clinical diseases (such as stroke, depression, schizophrenia, etc.
), and provides new research ideas for neuropharmacology
.
Research background White matter occupies about half of the brain's volume, and a complex structural connection network is established in the brain, which occupies an important position in the transmission of neural information
.
Neuroscience evidence shows that differences in brain white matter connections are related to a variety of neuronuclear psychiatric diseases, such as attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), schizophrenia, and Alzheimer's disease
.
At present, white matter microstructure differences and abnormalities can be obtained from dMRI data.
Commonly used indicators include FA (fractional anisotropy), MD (average diffusivity), AD (axial diffusivity), RD (radial diffusivity), MO (Anisotropic Mode)
.
Both family and population-based studies have reported that DTI data of white matter microstructure has high heritability
.
GWAS is used to identify genomes related to the variation of white matter microstructure between individuals, but these studies have two main limitations: 1) The sample size is small, the largest dMRI phenotype GWAS sample size previously published is 17,706 people, a large sample The quantity is essential to improve the genetic variation of white matter with weak effects and reveal the common genetic influence with other complex traits; 2).
Spatial specificity
.
Because the differences and changes in the white matter microstructure may not have a consistent pattern throughout the pathway, GWAS studies based on the whole brain may lose information
.
The genetic influence shared between the white matter of the brain and other complex features is unknown, and explaining these underlying genetic connections may identify important brain regions that are related to clinical disease
.
Summary of research methods Data sources: UK Biobank (UKB), Adolescent Brain Cognitive Development (ABCD), HCP, Pediatric Imaging, Neurocognition and Genetics (PING), Philadelphia Neurodevelopmental Cohort Analysis (PNC)
.
Using ENIGMA-DTI imaging to obtain voxel digital ground imaging images of 43802 subjects, focusing on 21 pre-defined white matter tracts, analyzing 5 main DTI measurements, and generating 215 DTI phenotypes for each individual
.
Then, GWSA analysis was performed on 215 phenotypes to discover the genetic structure of the white matter of the brain and explore the genetic connection with a large number of clinical endpoints in different characteristic areas
.
Research results (1) Figure 1 shows the heritability of SNP (Single Nucleotide Polymorphism) for 215 DTI parameters, including 110 mean values and 105 functional blocks, using UKB phase 1 and phase 2 data, respectively, and the two groups of inheritance The correlation between force estimates is 0.
92, and heritability analysis shows that the main microstructural variations of voxel FA maps collected by unconventional specific fiber bundle cells are controlled by genetics
.
Figure 1 SNP heritability of 215 DTI parameters, including 110 mean parameters and 105 functional blocks, as well as the genomic region that affects DTI parameters
.
Each signal point indicates that at least one of the 10 DTI parameters in the region is related to the genomic region (2) Figures 2 and 3 show the gene loci related to DTI parameters and brain diseases
.
Glioma risk variants are associated with multiple white matter tracts, including the corpus callosum, internal capsule, upper longitudinal tract, and anterior radiation crown
.
In gliomas, reduced white matter microstructure integrity and structural connectivity changes are observed
.
10q24.
33, 9p21.
3, and 2q33.
2 are co-localized with stroke and are associated with cardiovascular disease, type 2 diabetes, obesity, and hypertension, indicating that the genetic relationship between white matter microstructure and stroke may be partly through vascular risk factors Mediated
.
17q21.
31, 6p22.
1 and 6p22.
2y co-localize with many risk variants of brain diseases and cognition, such as schizophrenia, Alzheimer's disease, cortical basal degeneration, Parkinson's disease, depression, alcohol use disorders, mental and cognitive ability
.
In general, genetic links have been found between the microstructure of the white matter of the brain and a wide range of clinical outcomes and complex features
.
Combining white matter GWAS with these clinical results may help us understand the underlying mechanisms leading to these results
.
Figure 2 DTI parameters and brain disease-related colocalized gene loci Figure B.
shows the genetic effects of five colocalized glioma GWAS index variants (rs634537, rs2235573, rs55705857, rs3751667, rs723527) on FA
.
D.
Shows the voxel-based genetic effects of three co-localized stroke GWAS index variants (rs2295786, rs7859727, rs18818651) on macular degeneration Figure 3 Paired genetic correlations between DTI parameters of the white matter tracts and brain diseases and cognitive functions Sex
.
The position of the white matter tract and DTI parameters are related to stroke, MDD, intelligence and reaction time (D, E) (3) In addition, the author found that 14 white matter-related genes are targets of 79 drugs, of which 23 are antipsychotic drugs, 38 One is antidepressant drugs, eight are used to treat addiction disorders, six are anti-Parkinson's disease drugs, and four are anti-dementia drugs
.
In the area enrichment analysis of the 70 complex traits studied in the genetic correlation analysis, it was found that the heritability of 49 complex traits was significantly increased, mainly concentrated in the areas affecting the microstructure of the white matter of the brain (Figure 4)
.
Common mutations related to white matter microstructure change the function of regulatory elements in glial cells (especially oligodendrocytes), indicating that genes with higher transcription levels in brain tissue also have a stronger genetic association with DTI parameters
.
Figure 4 The full text summary of the enrichment analysis of brain cell types by partition: In summary, in this study, the author collected magnetic resonance imaging data of 43,805 subjects from 5 public data sources, and analyzed the genetic structure of brain white matter
.
Finally, 109 newly discovered genomic regions with white matter microstructure differences were identified, and many brain diseases and cognitive dysfunctions were found to be related to the genetic characteristics of white matter.
These results can help clinicians and basic researchers in the onset of brain diseases.
In-depth understanding of the mechanism
.
This article shows the value of large-scale neuroimaging data integration and the application of specific FPCA in the study of human brain genetics.
Identifying the cross-group and group-specific components of human brain genetic factors may be a hot research topic in the future
.
In addition, the authors found that some commonly used centrally acting drugs have effects on white matter microstructure-related genes, indicating that the neuropharmacology of brain diseases may benefit from the relationship between these drugs and white matter, which may bring new ideas to neuropharmacology research From a perspective, it is expected to improve the efficacy of drugs
.
(Link to full text: DOI: 10.
1126/science.
abf3736) Contact for questions related to Shengxin: 18501230653 (same number on WeChat) Welcome to follow Shengxin human transcriptome | methylation | resequencing | single cell | m6A | multiomics cytoscape | limma | WGCNA | Water Bear Legend | Linux Electrophoresis | PCR | A Brief History of Sequencing | Karyotype | NIPT | Basic Experimental Genes | 2019-nCoV | Enrichment Analysis | Joint Analysis | Microenvironmental Plague Pursuit | Summary of Ideas | Scholars | Scientific Research | Retraction | PhD Reading | Work
