Genome-wide association studies of brain structure and function in the UK Biobank

Elliott, Lloyd T.; Sharp, Kevin; Alfaro-Almagro, Fidel; Shi, Sinan; Miller, Karla L.; Douaud, Gwenaëlle; Marchini, Jonathan; Smith, Stephen M.

doi:10.1101/178806

Cited by 36 publications

(39 citation statements)

References 99 publications

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“…The current findings therefore indicate a potentially risk-conferring role for white matter over other modalities. This finding is supported by previous evidence that white matter microstructure has stronger phenotypic associations with lifetime depression compared to brain structural volumes 29 and higher SNP-heritability (20-60%) compared with other neuroimaging modalities, indicating a greater genomic contribution to individual differences in phenotypes 49 . The present paper provides evidence of associations of white matter microstructure with depression-PRS, however, these brain measures have a relatively low spatial resolution.…”

Section: Discussionsupporting

confidence: 82%

“…The present paper provides evidence of associations of white matter microstructure with depression-PRS, however, these brain measures have a relatively low spatial resolution. Recent gene expression studies suggest that genetic predisposition may influence more spatially and functionally specific, neuronal-level activities such as synaptic pruning and the overproduction of synapses 50 for regional segregation 51 during the process of brain maturation and myelin repair which contribute largely to brain structural and functional individual variance 49 . These highly regional and functionally specific brain phenotypes are of great importance and may help explain how genetic predisposition contributes to variance in neuroimaging measures.…”

Section: Discussionmentioning

confidence: 99%

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A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Shen

Howard

Adams

et al. 2019

Preprint

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AbstractDepression is the leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Depression is known to be heritable with a polygenic architecture, and results from genome-wide associations studies are providing summary statistics with increasing polygenic signal that can be used to estimate genetic risk scores for prediction in independent samples. This provides a timely opportunity to identify traits that are associated with polygenic risk of depression in the large and consistently phenotyped UK Biobank sample. Using the Psychiatric Genomics Consortium (PGC), 23andMe and non-imaging UK Biobank datasets as reference samples, we estimated polygenic risk scores for depression (depression-PRS) in a discovery sample of 10,674 people and a replication sample of 11,214 people from the UK Biobank Imaging Study, testing for associations with 210 behavioural and 278 neuroimaging phenotypes. In the discovery sample, 93 traits were significantly associated with depression-PRS after multiple testing correction. Among these, 92 traits were in the same direction, and 69 were significant in the replication analysis. For imaging traits that replicated across samples, higher depression-PRS was associated with lower global white matter microstructure, association-fibre and thalamic-radiation microstructural integrity (absolute β: 0.023 to 0.040, PFDR: 0.045 to 3.92×10-4). Mendelian Randomisation analysis showed a causal effect of liability to depression on these structural brain measures (β: 0.125 to 0.707, pFDR<0.048). Replicated behavioural traits that positively associated with depression-PRS included sleep problems, smoking status, measures of pain and stressful life experiences, and those negatively associated with depression-PRS included subjective ratings of physical health (absolute β: 0.014 to 0.180, PFDR: 0.046 to 8.54×10-15). Effect of depression PRS on mental health in the presence of reported childhood trauma, stressful life events and those living in more socially deprived areas showed increased variance explained by 1.42 – 4.08 times (pFDR for their interaction with depression-PRS: 0.049 to 0.003). Overall, the present study revealed replicable associations between depression-PRS and white matter microstructure that appeared to be a causal consequence of liability to depression. Analyses provided further evidence that greater effects of polygenic risk of depression are found in individuals exposed to risk-conferring environments.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Shen

Howard

Adams

et al. 2019

Preprint

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“…NGS technologies have enabled the production and deposit of vast amounts of whole genomes into public repositories [36][37][38] ushering the field of genomics into era of big data. This has in turn increased the scale of genomic studies from the analysis of single or few genomes to an ever-increasing large number of genomes [39,40].…”

Section: Bioinformatics Tools and Phylogenetic Toolsmentioning

confidence: 99%

Mosquito-Borne Viral Diseases: Control and Prevention in the Genomics Era

Fonseca¹,

Xavier²,

James³

et al. 2020

Vector-Borne Diseases - Recent Developments in Epidemiology and Control

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Mosquito-borne viral diseases are infections transmitted by the bite of infected mosquitoes. The burden of these diseases is highest in tropical and subtropical areas and they disproportionately affect the poorest populations. Since 2014, major outbreaks of dengue, chikungunya, yellow fever and Zika have afflicted populations and overwhelmed health systems in many countries. Distribution of mosquito-borne diseases is determined by complex demographic, environmental and social factors, causing diseases to emerge in countries where they were previously unknown. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. Considering pathogen surveillance in mind, real-time sequencing, bioinformatics tools and the combination of genomic and epidemiological data from viral infections can give essential information for understanding the past and the future of an epidemic, making possible to establish an effective surveillance framework on tracking the spread of infections to other geographic regions.

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“…As has been noted for other phenotypes from structure MRI and diffusion MRI, very large, highly powered and representative samples are difficult to collect at a single site (Ioannidis, 2014; Jahanshad, et al, 2013), although there are some notable exceptions, e.g. the UK Biobank (Elliott, et al, 2017). Multi-site studies can collect larger and more representative samples but require pre-processing steps to address site-specific sources of methodological variance.…”

Section: Introductionmentioning

confidence: 99%

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

Adhikari

Jahanshad²,

Shukla

et al. 2018

Human Brain Mapping

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We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 28.8-min sequence (spatial resolution = 2 × 2 × 2 mm ). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. We observed significant heritability (h = 0.2-0.4, p < .05) for functional connections from seven networks across both cohorts, with a significant positive correlation between heritability estimates across two cohorts. The similarity in heritability estimates for resting state connectivity measurements suggests that the additive genetic contribution to functional connectivity is robustly detectable across populations and imaging acquisition parameters. The overarching genetic influence, and means to consistently detect it, provides an opportunity to define a common genetic search space for future gene discovery studies.

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Genome-wide association studies of brain structure and function in the UK Biobank

Cited by 36 publications

References 99 publications

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Mosquito-Borne Viral Diseases: Control and Prevention in the Genomics Era

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

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