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Biology subjects

Joanna M Wardlaw

Publications and source records attributed to Joanna M Wardlaw.

5 recordsLinked to original sources

Age differences in brain white matter microstructure in UK Biobank (N = 3,513)

Quantifying the microstructural properties of the human brains connections is necessary for understanding normal ageing and disease states. We examined brain white matter MRI data in 3,513 generally healthy people aged 45-75 years from the UK Biobank sample. Using conventional water diffusion measures and newer, as-yet rarely-studied indices from neurite orientation dispersion and density imaging (NODDI), we document large age differences in white matter microstructure. Mean diffusivity was the most age-sensitive diffusion measure, with negative age associations strongest in the thalamic radiation and association fibres. Inter-individual differences in white matter microstructure across brain tracts become increasingly correlated in older age. This connectivity de-differentiation may reflect an age-related aggregation of systemic detrimental effects on the brain. We report several other novel results, including comparative age associations with volumetric indices and associations with hemisphere and sex. Results from this unusually large, single-scanner sample provide one of the most definitive characterisations to date of age differences in major white matter tracts in the human brain.\n\nAbbreviations

Neuroscience

Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared

Joint analysis of data from multiple studies in collaborative efforts strengthens scientific evidence, with the gold standard approach being the pooling of individual participant data (IPD). However, sharing IPD often has legal, ethical, and logistic constraints for sensitive or high-dimensional data, such as in clinical trials, observational studies, and large-scale omics studies. Therefore, meta-analysis of study-level effect estimates is routinely done, but this compromises on statistical power, accuracy, and flexibility. Here we propose a novel meta-analytical approach, named partial derivatives meta-analysis, that is mathematically equivalent to using IPD, yet only requires the sharing of aggregate data. It not only yields identical results as pooled IPD analyses, but also allows post-hoc adjustments for covariates and stratification without the need for site-specific re-analysis. Thus, in case that IPD cannot be shared, partial derivatives meta-analysis still produces gold standard results, which can be used to better inform guidelines and policies on clinical practice.

Bioinformatics

Does crossed aphasia originate from developmental disorders? A Mini-review and case study

Cognitive impairments associated with crossed aphasia were investigated in a single case study and a review of the literature. A review of literature identifies 4 main cognitive co-morbidities that are significantly associated with crossed aphasia. We present a case of confirmed crossed aphasia with dyslexia and dysgraphia, in which the latter two cannot be fully explained by the current lesion and are probable developmental disorders (dyslexia/dysgraphia). Extensive longitudinal cognitive investigations and a series of advanced imaging techniques (structural and functional) were used to investigate the cognitive and neuroanatomical basis of crossed aphasia and associated impairments in this patient. Using the results from the literature review and the single case study, we suggest that developmental disorders can be an underlying cause of partial right lateralisation shift of language processes, thereby supporting the theory that developmental disorders can be an underlying cause of crossed aphasia.\n\nHighlightsO_LICentral apraxia, dysgraphia, hemi-neglect & acalculia associated with CA\nC_LIO_LIDevelopmental disorders can underlie partial right lateralisation shift\nC_LIO_LIDysfunction of left hemisphere can cause crossed aphasia\nC_LIO_LIClinically, pre-morbid impairments must be investigated in CA cases\nC_LI

Neuroscience

Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N = 112 151) and 24 GWAS consortia.

The causes of the known associations between poorer cognitive function and many adverse neuropsychiatric outcomes, poorer physical health, and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric, and cognitive traits in the participants of UK Biobank, a very large population-based sample (N = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics applied to the method of linkage disequilibrium regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, BMI, intracranial volume, infant head circumference, and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.

Genetics

Molecular genetic contributions to self-rated health

BackgroundPoorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.\n\nMethodsWe carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.\n\nResultsThe GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77x10-10) close to KLF7, which has previously been associated with obesity and type 2 diabetes. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15x10-10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.\n\nConclusionsIndividual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.\n\nKey MessagesO_LIGenetic variants associated with common diseases and psychological traits are associated with self-rated health.\nC_LIO_LIThe SNP-based heritability of self-rated health is 0.13 (SE 0.006).\nC_LIO_LIThere is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.\nC_LI

Genetics