Publications by authors named "Anders M Dale"

473 Publications

Corrigendum to "Microstructural development from 9 to 14 years: Evidence from the ABCD Study" [Dev. Cognit. Neurosci. 53 (2022) 101044].

Dev Cogn Neurosci 2022 Jan 13:101063. Epub 2022 Jan 13.

Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA.

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http://dx.doi.org/10.1016/j.dcn.2022.101063DOI Listing
January 2022

Correcting B inhomogeneity-induced distortions in whole-body diffusion MRI of bone.

Sci Rep 2022 Jan 7;12(1):265. Epub 2022 Jan 7.

Department of Radiation Medicine and Applied Sciences, School of Medicine, University of California San Diego, 9500 Gilman Drive, Mail Code 0861, La Jolla, CA, 92093-0861, USA.

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T-weighted images were annotated. Changes in distance between DWI- and T-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10). On average (± SD), participants' largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0-7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10). These findings support the use of distortion correction techniques to improve localization of bone on DWI.
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http://dx.doi.org/10.1038/s41598-021-04467-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741963PMC
January 2022

Associations Between MRI-Assessed Locus Coeruleus Integrity and Cortical Gray Matter Microstructure.

Cereb Cortex 2021 Dec 31. Epub 2021 Dec 31.

Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA.

The locus coeruleus (LC) is one of the earliest sites of tau pathology, making it a key structure in early Alzheimer's disease (AD) progression. As the primary source of norepinephrine for the brain, reduced LC integrity may have negative consequences for brain health, yet macrostructural brain measures (e.g. cortical thickness) may not be sensitive to early stages of neurodegeneration. We therefore examined whether LC integrity was associated with differences in cortical gray matter microstructure among 435 men (mean age = 67.5; range = 62-71.7). LC structural integrity was indexed by contrast-to-noise ratio (LCCNR) from a neuromelanin-sensitive MRI scan. Restriction spectrum imaging (RSI), an advanced multi-shell diffusion technique, was used to characterize cortical microstructure, modeling total diffusion in restricted, hindered, and free water compartments. Higher LCCNR (greater integrity) was associated with higher hindered and lower free water diffusion in multiple cortical regions. In contrast, no associations between LCCNR and cortical thickness survived correction. Results suggest lower LC integrity is associated with patterns of cortical microstructure that may reflect a reduction in cytoarchitectural barriers due to broader neurodegenerative processes. These findings highlight the potential utility for LC imaging and advanced diffusion measures of cortical microstructure in assessing brain health and early identification of neurodegenerative processes.
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http://dx.doi.org/10.1093/cercor/bhab475DOI Listing
December 2021

The genetic architecture of human cortical folding.

Sci Adv 2021 Dec 15;7(51):eabj9446. Epub 2021 Dec 15.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

[Figure: see text].
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http://dx.doi.org/10.1126/sciadv.abj9446DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673767PMC
December 2021

Characterization of the diffusion signal of breast tissues using multi-exponential models.

Magn Reson Med 2021 Dec 14. Epub 2021 Dec 14.

Department of Radiology, University of California San Diego, La Jolla, California, USA.

Purpose: Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues.

Methods: The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.

Results: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D = 0, D = 1.5 × 10 , and D = 10.8 × 10 mm /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent.

Conclusion: Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.
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http://dx.doi.org/10.1002/mrm.29090DOI Listing
December 2021

Microstructural development from 9 to 14 years: Evidence from the ABCD Study.

Dev Cogn Neurosci 2022 Feb 3;53:101044. Epub 2021 Dec 3.

Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA.

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.
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http://dx.doi.org/10.1016/j.dcn.2021.101044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8671104PMC
February 2022

Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information.

Biol Psychiatry 2021 Sep 28. Epub 2021 Sep 28.

Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; McLean Hospital, Belmont, Massachusetts.

Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs).

Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms.

Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program.

Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.
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http://dx.doi.org/10.1016/j.biopsych.2021.09.020DOI Listing
September 2021

Paradoxical cognitive trajectories in men from earlier to later adulthood.

Neurobiol Aging 2022 01 14;109:229-238. Epub 2021 Oct 14.

Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA.

Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.10.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715388PMC
January 2022

Genetic Stratification of Age-Dependent Parkinson's Disease Risk by Polygenic Hazard Score.

Mov Disord 2022 Jan 6;37(1):62-69. Epub 2021 Oct 6.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Background: Parkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders.

Objectives: We aimed to develop and validate a polygenic hazard score model in sporadic PD.

Methods: Using a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls.

Results: A polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups.

Conclusions: We demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.28808DOI Listing
January 2022

Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life.

Addiction 2021 Oct 4. Epub 2021 Oct 4.

Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA.

Background And Aims: Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later.

Design: Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated.

Setting: Population-based United States sample.

Participants/cases: Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves.

Measurements: Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19).

Findings: In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD.

Conclusions: Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.
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http://dx.doi.org/10.1111/add.15710DOI Listing
October 2021

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology.

Neuroimage 2021 12 21;244:118603. Epub 2021 Sep 21.

NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo University Hospital, Ullevål, PO Box 4956 Nydalen, Oslo 0424, Norway; Department of Neurosciences, University of California San Diego, La Jolla, CA 92037, USA; Department of Radiology, University of California San Diego, La Jolla, CA 92037, USA; Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92037, USA. Electronic address:

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118603DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785963PMC
December 2021

Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age.

Neurobiol Aging 2021 12 19;108:80-89. Epub 2021 Aug 19.

Department of Psychiatry, University of California San Diego, San Diego, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, San Diego, CA, USA.

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.08.007DOI Listing
December 2021

Characterising the shared genetic determinants of bipolar disorder, schizophrenia and risk-taking.

Transl Psychiatry 2021 09 8;11(1):466. Epub 2021 Sep 8.

NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.

Increased risk-taking is a central component of bipolar disorder (BIP) and is implicated in schizophrenia (SCZ). Risky behaviours, including smoking and alcohol use, are overrepresented in both disorders and associated with poor health outcomes. Positive genetic correlations are reported but an improved understanding of the shared genetic architecture between risk phenotypes and psychiatric disorders may provide insights into underlying neurobiological mechanisms. We aimed to characterise the genetic overlap between risk phenotypes and SCZ, and BIP by estimating the total number of shared variants using the bivariate causal mixture model and identifying shared genomic loci using the conjunctional false discovery rate method. Summary statistics from genome wide association studies of SCZ, BIP, risk-taking and risky behaviours were acquired (n = 82,315-466,751). Genomic loci were functionally annotated using FUMA. Of 8.6-8.7 K variants predicted to influence BIP, 6.6 K and 7.4 K were predicted to influence risk-taking and risky behaviours, respectively. Similarly, of 10.2-10.3 K variants influencing SCZ, 9.6 and 8.8 K were predicted to influence risk-taking and risky behaviours, respectively. We identified 192 loci jointly associated with SCZ and risk phenotypes and 206 associated with BIP and risk phenotypes, of which 68 were common to both risk-taking and risky behaviours and 124 were novel to SCZ or BIP. Functional annotation implicated differential expression in multiple cortical and sub-cortical regions. In conclusion, we report extensive polygenic overlap between risk phenotypes and BIP and SCZ, identify specific loci contributing to this shared risk and highlight biologically plausible mechanisms that may underlie risk-taking in severe psychiatric disorders.
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http://dx.doi.org/10.1038/s41398-021-01576-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426401PMC
September 2021

Characterizing the Genetic Overlap Between Psychiatric Disorders and Sleep-Related Phenotypes.

Biol Psychiatry 2021 11 14;90(9):621-631. Epub 2021 Jul 14.

NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, University of Oslo, Oslo, Norway. Electronic address:

Background: A range of sleep disturbances are commonly experienced by patients with psychiatric disorders, and genome-wide genetic analyses have shown some significant genetic correlations between these traits. Here, we applied novel statistical genetic methodologies to better characterize the potential shared genetic architecture between sleep-related phenotypes and psychiatric disorders.

Methods: Using the MiXeR method, which can estimate polygenic overlap beyond genetic correlation, the shared genetic architecture between major psychiatric disorders (bipolar disorder [N = 51,710], depression [N = 480,359], and schizophrenia [N = 77,096]) and sleep-related phenotypes (chronotype [N = 449,734], insomnia [N = 386,533] and sleep duration [N = 446,118]) were quantified on the basis of genetic summary statistics. Furthermore, the conditional/conjunctional false discovery rate framework was used to identify specific shared loci between these phenotypes, for which positional and functional annotation were conducted with FUMA.

Results: Extensive genetic overlap between the sleep-related phenotypes and bipolar disorder (63%-77%), depression (76%-79%), and schizophrenia (64%-79%) was identified, with moderate levels of congruence between most investigated traits (47%-58%). Specific shared loci were identified for all bivariate analyses, and a subset of 70 credible genes were mapped to these shared loci.

Conclusions: The current results provide evidence for substantial polygenic overlap between psychiatric disorders and sleep-related phenotypes, beyond genetic correlation (|r| = 0.02 to 0.42). Moderate congruency within the shared genetic components suggests a complex genetic relationship and potential subgroups with higher or lower genetic concordance. This work provides new insights and understanding of the shared genetic etiology of sleep-related phenotypes and psychiatric disorders and highlights new opportunities and avenues for future investigation.
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http://dx.doi.org/10.1016/j.biopsych.2021.07.007DOI Listing
November 2021

Genome-wide analysis reveals genetic overlap between alcohol use behaviours, schizophrenia and bipolar disorder and identifies novel shared risk loci.

Addiction 2021 Sep 2. Epub 2021 Sep 2.

NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.

Background And Aim: Schizophrenia (SCZ) and bipolar disorder (BD) have a high comorbidity of alcohol use disorder (AUD), and both comorbid AUD and excessive alcohol consumption (AC) have been linked to greater illness severity. We aimed to identify genomic loci jointly associated with SCZ, BD, AUD and AC to gain further insights into their shared genetic architecture.

Design: We analysed summary data (P values and Z scores) from genome-wide association studies (GWAS) using conjunctional false discovery rate (conjFDR) analysis, which increases power to discover shared genomic loci. We functionally characterized the identified loci using publicly available biological resources.

Setting: AUD and AC data provided by the Million Veteran Program, derived from the United States Department of Veterans Affairs Healthcare System. SCZ and BD data provided by the Psychiatric Genomics Consortium, based on cohorts from countries in Europe, North America and Australia.

Participants: AUD (34 658 cases, 167 346 controls), AC (n = 200 680), SCZ (31 013 cases and 38 918 controls), BD (20 352 cases and 31 358 controls). All participants were of European ancestry.

Measurements: Genomic loci shared between alcohol traits, SCZ and BD at conjFDR <0.05.

Findings: Conditional Q-Q plots showed single-nucleotide polymorphism (SNP) enrichment for both alcohol traits across different levels of significance with SCZ and BD, and vice versa. Using conjFDR analysis we leveraged this genetic enrichment and identified several loci shared between SCZ and AUD (n = 28) and AC (n = 24), BD and AUD (n = 2) and AC (n = 8) at conjFDR <0.05. Among these loci, 24 are novel for AUD, 15 are novel for AC, three are novel for SCZ and one is novel for BD. There was a mixture of same and opposite effect directions among the shared loci.

Conclusions: Alcohol use disorder and alcohol consumption share genomic loci with the psychiatric disorders schizophrenia and bipolar disorder with a mixed pattern of effect directions, indicating a complex genetic relationship between the phenotypes.
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http://dx.doi.org/10.1111/add.15680DOI Listing
September 2021

Sex differences in Alzheimer's disease: do differences in tau explain the verbal memory gap?

Neurobiol Aging 2021 11 4;107:70-77. Epub 2021 Jun 4.

Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA.

To determine if sex differences in verbal memory in AD are related to differences in extent or distribution of pathological tau, we studied 275 participants who were amyloid PET positive and carried clinical classifications of normal cognition, mild cognitive impairment (MCI) or dementia, and had tau (AV1451) PET. We compared tau distribution between men and women, and as a function of genetic risk. In MCI we further explored the relationship between quantity and distribution of tau in relation to verbal memory scores. Women had more tau burden overall, but this was driven by sex differences at the MCI stage. There was no significant difference in tau load by APOE e4 status. Within the MCI group the association between tau and performance in verbal memory tasks was stronger in women than men. The topography of the associations between tau and verbal memory also differed in MCI; women demonstrated stronger relationships between tau distribution and verbal memory performance, especially in the left hemisphere. These findings have implications for understanding tau distribution and spread, and in interpretation of verbal memory performance.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.05.013DOI Listing
November 2021

12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56.

Brain Commun 2021 23;3(3):fcab167. Epub 2021 Jul 23.

Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA.

Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (s = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%;  = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.
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http://dx.doi.org/10.1093/braincomms/fcab167DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361427PMC
July 2021

Extensive bidirectional genetic overlap between bipolar disorder and cardiovascular disease phenotypes.

Transl Psychiatry 2021 07 23;11(1):407. Epub 2021 Jul 23.

NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Patients with bipolar disorder (BIP) have a high risk of cardiovascular disease (CVD), despite considerable individual variation. The mechanisms underlying comorbid CVD in BIP remain largely unknown. We investigated polygenic overlap between BIP and CVD phenotypes, including CVD risk factors and coronary artery disease (CAD). We analyzed large genome-wide association studies of BIP (n = 51,710) and CVD phenotypes (n = 159,208-795,640), using bivariate causal mixture model (MiXeR), which estimates the total amount of shared genetic variants, and conjunctional false discovery rate (FDR), which identifies specific overlapping loci. MiXeR revealed polygenic overlap between BIP and body mass index (BMI) (82%), diastolic and systolic blood pressure (20-22%) and CAD (11%) despite insignificant genetic correlations. Using conjunctional FDR < 0.05, we identified 129 shared loci between BIP and CVD phenotypes, mainly BMI (n = 69), systolic (n = 53), and diastolic (n = 53) blood pressure, of which 22 are novel BIP loci. There was a pattern of mixed effect directions of the shared loci between BIP and CVD phenotypes. Functional analyses indicated that the shared loci are linked to brain-expressed genes and involved in neurodevelopment, lipid metabolism, chromatin assembly/disassembly and intracellular processes. Altogether, the study revealed extensive polygenic overlap between BIP and comorbid CVD, implicating shared molecular genetic mechanisms. The mixed effect directions of the shared loci suggest variation in genetic susceptibility to CVD across BIP subgroups, which may underlie the heterogeneity of CVD comorbidity in BIP patients. The findings suggest more focus on targeted lifestyle interventions and personalized pharmacological treatment to reduce CVD comorbidity in BIP.
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http://dx.doi.org/10.1038/s41398-021-01527-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8302675PMC
July 2021

The Neurobiological Effects of Electroconvulsive Therapy Studied Through Magnetic Resonance: What Have We Learned, and Where Do We Go?

Biol Psychiatry 2021 May 31. Epub 2021 May 31.

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway. Electronic address:

Electroconvulsive therapy (ECT) is an established treatment choice for severe, treatment-resistant depression, yet its mechanisms of action remain elusive. Magnetic resonance imaging (MRI) of the human brain before and after treatment has been crucial to aid our comprehension of the ECT neurobiological effects. However, to date, a majority of MRI studies have been underpowered and have used heterogeneous patient samples as well as different methodological approaches, altogether causing mixed results and poor clinical translation. Hence, an association between MRI markers and therapeutic response remains to be established. Recently, the availability of large datasets through a global collaboration has provided the statistical power needed to characterize whole-brain structural and functional brain changes after ECT. In addition, MRI technological developments allow new aspects of brain function and structure to be investigated. Finally, more recent studies have also investigated immediate and long-term effects of ECT, which may aid in the separation of the therapeutically relevant effects from epiphenomena. The goal of this review is to outline MRI studies (T1, diffusion-weighted imaging, proton magnetic resonance spectroscopy) of ECT in depression to advance our understanding of the ECT neurobiological effects. Based on the reviewed literature, we suggest a model whereby the neurobiological effects can be understood within a framework of disruption, neuroplasticity, and rewiring of neural circuits. An improved characterization of the neurobiological effects of ECT may increase our understanding of ECT's therapeutic effects, ultimately leading to improved patient care.
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http://dx.doi.org/10.1016/j.biopsych.2021.05.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8630079PMC
May 2021

Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools.

Brain 2021 Jul 17. Epub 2021 Jul 17.

NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407 Oslo, Norway.

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine's polygenic architecture overlaps with bipolar disorder, depression, and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases=59,674; n controls=316,078), bipolar disorder (n cases=20,352; n controls=31,358), depression (n cases=170,756; n controls=328,443) and schizophrenia (n cases=40,675, n controls=64,643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterised to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8K disorder-influencing variants) compared to mental disorders (8.1K-12.3K disorder-influencing variants). Bivariate analysis estimated that 0.8K (0.3K), 2.1K (SD = 0.1K) and 2.3K (SD = 0.3K) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1.8K, SD = 0.3K) and educational attainment (2.1K, SD = 0.3K) but not height (1K, SD = 0.1K). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2, SLC9B1. Gene-set analysis identified several putative gene-sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of 'pleiotropic' variants which influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation.
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http://dx.doi.org/10.1093/brain/awab267DOI Listing
July 2021

Examining Individual and Synergistic Contributions of PTSD and Genetics to Blood Pressure: A Trans-Ethnic Meta-Analysis.

Front Neurosci 2021 23;15:678503. Epub 2021 Jun 23.

Department of Psychiatry, Case Western Reserve University, Cleveland, OH, United States.

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European ( = 70,870) and African ( = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, < 1E-20; DBP: β = 1.32, SE = 0.04, < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study - United Kingdom Biobank - PTSD symptoms were negatively associated with SBP levels (β = -1.46, SE = 0.44, = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.
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http://dx.doi.org/10.3389/fnins.2021.678503DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262489PMC
June 2021

Elevated body weight modulates subcortical volume change and associated clinical response following electroconvulsive therapy.

J Psychiatry Neurosci 2021 07 5;46(4):E418-E426. Epub 2021 Jul 5.

From the Institute for Translational Psychiatry, University of Münster, Münster, Germany (Opel, Repple, Dannlowski, Redlich); Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany (Kavakbasi, Baune); the Departments of Neurology, Psychiatry, and Biobehavioral Sciences, University of California, Los Angeles, CA (Narr); the Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM (Abbott); the Institute of Behavioral Science, Feintein Institutes for Medical Research, Manhasset, NY (Argyelan); the Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, NY (Argyelan); the Department of Psychiatry, University of California, Los Angeles (Espinoza); the Department of Geriatric Psychiatry, University Psychiatric Center KU Leuven, KU Leuven, Leuven, Belgium (Emsell, Vandenbulcke); the KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry & Geriatric Psychiatry, University Psychiatric Center KU Leuven, Belgium (Bouckaert); the Academic Center for ECT and Neurostimulation (AcCENT), University Psychiatric Center (UPC)-KU Leuven, Kortenberg, Belgium (Sienaert); the Center for Social and Affective Neuroscience, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden (Nordanskog); the Psychiatric Center Copenhagen (Rigshospitalet), Mental Health Services of the Capital Region of Denmark, Copenhagen, Denmark (Jorgensen); the Neurobiology Research Unit, Rigshospitalet and University of Copenhagen, Denmark (Paulson); the Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark (Hanson); the Center for Magnetic Resonance, Department of Health Technology, Technical University of Denmark, Kgs, Lyngby, Denmark (Hanson); the GGZ in Geest Specialized Mental Health Care, Amsterdam, the Netherlands (Dols, Van Exel, Oudega); the Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands (Dols, van Exel, Oudega); the Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan (Takamiya, Kishimoto); the Department of Radiology, Haukeland University Hospital, Bergen, Norway (Ousdal); the Department of Biomedicine, University of Bergen, Bergen, Norway (Haavik); the Division of Psychiatry, Haukeland University Hospital, Bergen, Norway (Haavik, Hammar); the Department of Biological and Medical Psychology, University of Bergen, Norway (Hammar); the NORMENT, Department of Psychiatry, Haukeland University Hospital, Bergen, Norway (Oedegaard, Kessler); the Department of Clinical Medicine, University of Bergen, Bergen, Norway (Oedegaard, Kessler, Oltedal); the Department of Radiology, University of California, San Diego, La Jolla, California (Bartsch); the Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway (Bartsch, Oltedal); the Departments of Radiology, Neurosciences, and Psychiatry, University of California, San Diego (Dale); the Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California (Dale); the Department of Psychiatry, University of Melbourne, Melbourne, Australia (Baune); the The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia (Baune); and the Department of Psychology, University of Halle, Halle, Germany (Redlich).

Background: Obesity is a frequent somatic comorbidity of major depression, and it has been associated with worse clinical outcomes and brain structural abnormalities. Converging evidence suggests that electroconvulsive therapy (ECT) induces both clinical improvements and increased subcortical grey matter volume in patients with depression. However, it remains unknown whether increased body weight modulates the clinical response and structural neuroplasticity that occur with ECT.

Methods: To address this question, we conducted a longitudinal investigation of structural MRI data from the Global ECT-MRI Research Collaboration (GEMRIC) in 223 patients who were experiencing a major depressive episode (10 scanning sites). Structural MRI data were acquired before and after ECT, and we assessed change in subcortical grey matter volume using FreeSurfer and Quarc.

Results: Higher body mass index (BMI) was associated with a significantly lower increase in subcortical grey matter volume following ECT. We observed significant negative associations between BMI and change in subcortical grey matter volume, with pronounced effects in the thalamus and putamen, where obese participants showed increases in grey matter volume that were 43.3% and 49.6%, respectively, of the increases found in participants with normal weight. As well, BMI significantly moderated the association between subcortical grey matter volume change and clinical response to ECT. We observed no significant association between BMI and clinical response to ECT.

Limitations: Because only baseline BMI values were available, we were unable to study BMI changes during ECT and their potential association with clinical and grey matter volume change.

Conclusion: Future studies should take into account the relevance of body weight as a modulator of structural neuroplasticity during ECT treatment and aim to further explore the functional relevance of this novel finding.
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http://dx.doi.org/10.1503/jpn.200176DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8410473PMC
July 2021

Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness.

JAMA Psychiatry 2021 09;78(9):1020-1030

NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Importance: Schizophrenia is a complex heritable disorder associated with many genetic variants, each with a small effect. While cortical differences between patients with schizophrenia and healthy controls are consistently reported, the underlying molecular mechanisms remain elusive.

Objective: To investigate the extent of shared genetic architecture between schizophrenia and brain cortical surface area (SA) and thickness (TH) and to identify shared genomic loci.

Design, Setting, And Participants: Independent genome-wide association study data on schizophrenia (Psychiatric Genomics Consortium and CLOZUK: n = 105 318) and SA and TH (UK Biobank: n = 33 735) were obtained. The extent of polygenic overlap was investigated using MiXeR. The specific shared genomic loci were identified by conditional/conjunctional false discovery rate analysis and were further examined in 3 independent cohorts. Data were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021.

Main Outcomes And Measures: The primary outcomes were estimated fractions of polygenic overlap between schizophrenia, total SA, and average TH and a list of functionally characterized shared genomic loci.

Results: Based on genome-wide association study data from 139 053 participants, MiXeR estimated schizophrenia to be more polygenic (9703 single-nucleotide variants [SNVs]) than total SA (2101 SNVs) and average TH (1363 SNVs). Most SNVs associated with total SA (1966 of 2101 [93.6%]) and average TH (1322 of 1363 [97.0%]) may be associated with the development of schizophrenia. Subsequent conjunctional false discovery rate analysis identified 44 and 23 schizophrenia risk loci shared with total SA and average TH, respectively. The SNV associations of shared loci between schizophrenia and total SA revealed en masse concordant association between the discovery and independent cohorts. After removing high linkage disequilibrium regions, such as the major histocompatibility complex region, the shared loci were enriched in immunologic signature gene sets. Polygenic overlap and shared loci between schizophrenia and schizophrenia-associated regions of interest for SA (superior frontal and middle temporal gyri) and for TH (superior temporal, inferior temporal, and superior frontal gyri) were also identified.

Conclusions And Relevance: This study demonstrated shared genetic loci between cortical morphometry and schizophrenia, among which a subset are associated with immunity. These findings provide an insight into the complex genetic architecture and associated with schizophrenia.
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http://dx.doi.org/10.1001/jamapsychiatry.2021.1435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223140PMC
September 2021

Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24.

Prostate Cancer Prostatic Dis 2021 Jun 14. Epub 2021 Jun 14.

School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.

Background: We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.

Materials And Methods: Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.

Results: CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.

Conclusion: We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.
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http://dx.doi.org/10.1038/s41391-021-00403-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669040PMC
June 2021

All-Optical Electrophysiology in hiPSC-Derived Neurons With Synthetic Voltage Sensors.

Front Cell Neurosci 2021 28;15:671549. Epub 2021 May 28.

Department of Biomedical Engineering, Boston University, Boston, MA, United States.

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.
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http://dx.doi.org/10.3389/fncel.2021.671549DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193062PMC
May 2021

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

The genetic architecture of human complex phenotypes is modulated by linkage disequilibrium and heterozygosity.

Genetics 2021 03;217(3)

Department of Radiology, University of California, San Francisco, San Francisco, CA 94158, USA.

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWASs) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model-primarily through quantification of selection pressure-reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.
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http://dx.doi.org/10.1093/genetics/iyaa046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045737PMC
March 2021

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

J Magn Reson Imaging 2021 09 31;54(3):975-984. Epub 2021 Mar 31.

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California, USA.

Background: Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI -C , yielded greatest tumor conspicuity.

Purpose: To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI -C ) as a quantitative voxel-level classifier of PCa.

Study Type: Retrospective.

Subjects: Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.

Field Strength/sequence: A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.

Assessment: High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI -C and conventional ADC. Classifier images were also generated.

Statistical Tests: Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI -C was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.

Results: RSI -C outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).

Data Conclusion: RSI -C yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.

Evidence Level: 3 TECHNICAL EFFICACY: Stage 2.
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http://dx.doi.org/10.1002/jmri.27623DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8363567PMC
September 2021
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