Michelle Luciano’s research while affiliated with University of Edinburgh and other places

Personality trait similarity among ordinary relatives is surprisingly low, with parent-offspring and sibling-sibling correlations usually r ≤ .15. We explain why these correlations are biased in typical single-method studies and argue that this problem can only be addressed with multi-method designs. We also explain why ordinary relative comparisons can provide a more generalizable way of estimating (additive, narrow-sense) heritability than the better-known twin comparisons. In a sample of parent-offspring (Npairs = 522), sibling-sibling (Npairs = 388), and second-degree relative pairs (Npairs = 476), who rated their Big Five personality traits and life satisfaction and were each rated by an independent informant (Nparticipants = 2,258 + informants), we found that parent-offspring and sibling correlations were about one-third higher than typically shown (r ≈ .20). Based on the ordinary relative comparisons, the heritability of personality traits and life satisfaction was about 40%, compared with about 26% typical to self-report studies. Life satisfaction was as heritable as personality traits, sharing about 80% of its genetic variance with neuroticism, extraversion, and conscientiousness. About half of life satisfaction’s phenotypic correlations with neuroticism and extraversion and its entire correlation with conscientiousness were explained by shared genetic factors. Using data from a larger sample of relatives with only self-reports (Nparticipants = 32,004; Npairs = 24,118), we provide further evidence that growing up together does not make people more similar. The results were consistent for both aggregate traits and individual items. Only multi-method designs can accurately reveal traits’ similarity among relatives, and their genetic and environmental transmission.

Peak width of skeletonized mean diffusivity (PSMD) is an emerging automated diffusion imaging marker showing clinically relevant changes in cerebral small vessel disease (cSVD), a leading cause of stroke and dementia with no mechanism-based treatment. We conducted a genome-wide association study of PSMD in 58,403 participants from 24 population-based cohorts (89% European, 10% East-Asian, 1% African-American), identifying 31 independent common variant associations. Additionally, a whole-exome sequencing analysis in 32,957 participants yielded associations of PSMD with single and burden of rare coding variants in four novel genes. Mendelian randomization supported causal association of higher blood pressure with larger PSMD values, and of larger PSMD with an increased risk of stroke, especially intracerebral hemorrhage. Strikingly, genetic susceptibility to white matter hyperintensities, an established MRI-marker of cSVD, was associated with higher PSMD from early childhood to older age, with prominent lifespan effects for VCAN and SMG6 . Leveraging unique brain single-cell sequencing resources we showed temporal changes in the cell-type specificity of these genes in the developing brain and overall enrichment of PSMD risk loci in genes expressed in fetal brain endothelial cells. Finally, through extensive integration with multi-omics resources, we provide precious leads for gene prioritization to accelerate drug discovery for cSVD.

Background Frailty is a complex clinical state that is associated with poorer health outcomes and increased dementia risk in older adults. It is routinely measured using the Frailty Index, which is a proportional score based on the number of ‘deficits’ that an individual has. Whilst such measures are useful for risk assessment, the aggregation of highly heterogeneous deficit profiles in genetic studies may obscure important insights into the underlying biology of frailty. Method We used Genomic Structural Equation Modelling to model shared genetic signal between 30 deficits from the Frailty Index using summary data from genome‐wide association studies (GWAS). This involved conducting a multivariate GWAS to identify genomic risk loci associated with each of the latent frailty factors in our model. We used polygenic risk scores (PRS) to test the predictive accuracy of the latent factors for detecting frailty status in 3 external cohort studies. Finally, we tested the genetic correlation between our latent frailty factors and >50 aging‐related outcomes, including dementia. Result The genetic architecture of the frailty deficits was best captured by a general factor influencing all 30 deficits, plus 6 additional latent factors representing genetic overlap between distinct subsets of deficits that were uncorrelated with the general factor. These 6 factors can be broadly described as reflecting pathways linked to social isolation, unhealthy lifestyle, multimorbidity, metabolic/respiratory status, cognition, and disability. GWAS conducted to measure the effects of individual genetic variants on these frailty factors identified 408 genomic risk loci. Our PRS analyses demonstrated consistent evidence that shared genetic pathways between frailty deficits linked to multimorbidity and cognition, but unique of the general frailty factor, are particularly important for predicting frailty status. Genetic correlation analyses further highlighted a significant genetic association between frailty pathways linked to cognition and Alzheimer’s disease (rg = 0.32, SE = 0.07, p = 5.58×10⁻⁰⁶). Conclusion Our findings provide novel insights into the characterization of general and specific pathways within the frailty state at the genetic level. We demonstrate how refining our knowledge of frailty, particularly in relation to multimorbidity and cognitive impairment, may help to stratify frail individuals at increased risk of developing dementia or other adverse health outcomes.

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|β| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

Background Air pollution in later life has been associated with dementia; however, limited research has investigated the association between air pollution across the life course, either at specific life periods or cumulatively. The project investigates the association of air pollution with dementia via a life-course epidemiological approach. Methods Participants of the Lothian Birth Cohort, born in 1936, provided lifetime residential history in 2014. Participant’s air pollution exposure for time periods 1935, 1950, 1970, 1980, 1990, 2001, and 2007 was modeled using an atmospheric chemistry transport model. Lifetime cumulative exposures were calculated as time-weighted mean exposure. Of 572 participants, 67 developed all-cause dementia [35 with Alzheimer's dementia (AD)] by wave 5 (~82 years). Cox proportional hazards and competing risk models assessed the association between all-cause dementia and AD with particulate matter (diameter of ≤2.5 µm) PM 2.5 and nitrogen dioxide (NO 2 ) exposure at specific life periods and cumulatively. False discovery rate (FDR) correction was applied for multiple testing. Results The mean follow-up was 11.26 years. One standard deviation (SD) higher exposure to air pollution in 1935 (PM 2.5 = 14.03 μg/m ³ , NO 2 = 5.35 μg/m ³ ) was positively linked but not statistically significant to all-cause dementia [PM 2.5 hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 0.90, 1.49; NO 2 HR = 1.13, 95% CI = 0.88, 1.47] and AD (PM 2.5 HR = 1.38, 95% CI = 1.00, 1.91; NO 2 HR = 1.35, 95% CI = 0.92, 1.99). In the competing risk model, one SD elevated PM 2.5 exposure (1.12 μg/m ³ ) in 1990 was inversely associated with dementia (subdistribution HR = 0.82, 95% CI = 0.67, 0.99) at P = 0.034 but not after FDR correction ( P FDR = 0.442). Higher cumulative PM 2.5 per one SD was associated with an increased risk of all-cause dementia and AD for all accumulation models except for the early-life model. Conclusion The in-utero and early-life exposure to PM 2.5 and NO 2 was associated with higher AD and all-cause dementia risk, suggesting a sensitive/critical period. Cumulative exposure to PM 2.5 across the life course was associated with higher dementia risk. Midlife PM 2.5 exposure’s negative association with all-cause dementia risk may stem from unaddressed confounders or bias.

This study aimed to test theoretical predictions over biological underpinnings of previously documented phenotypic correlations between human language-related and musical rhythm traits. Here, after identifying significant genetic correlations between rhythm, dyslexia and various language-related traits, we adapted multivariate methods to capture genetic signals common to genome-wide association studies of rhythm (N = 606,825) and dyslexia (N = 1,138,870). The results revealed 16 pleiotropic loci (P < 5 × 10-8) jointly associated with rhythm impairment and dyslexia, and intricate shared genetic and neurobiological architectures. The joint genetic signal was enriched for foetal and adult brain cell-specific regulatory regions, highlighting complex cellular composition in their shared underpinnings. Local genetic correlation with a key white matter tract (the left superior longitudinal fasciculus-I) substantiated hypotheses about auditory-motor connectivity as a genetically influenced, evolutionarily relevant neural endophenotype common to rhythm and language processing. Overall, we provide empirical evidence of multiple aspects of shared biology linking language and musical rhythm, contributing novel insight into the evolutionary relationships between human musicality and linguistic communication traits.

A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e., a single MRI scan per participant. LBA was simply calculated based on the relationship between total brain volume (TBV) and intracranial volume (ICV), using three computationally distinct approaches: the difference (ICV-TBV), ratio (TBV/ICV), and regression-residual method (TBV~ICV). LBA derived with all three methods were substantially correlated with well-validated neuroradiological atrophy rating scales (r = 0.37-0.44). Compared with the difference or ratio method, LBA computed with the residual method most strongly captured phenotypic variance associated with cognitive decline (r = 0.36), frailty (r = 0.24), age-moderated brain shrinkage (r = 0.45), and longitudinally-measured atrophic changes (r = 0.36). LBA computed using a difference score was strongly correlated with baseline (i.e., ICV; r = 0.81) and yielded GWAS signal similar to ICV (rg = 0.75). We performed the largest genetic study of LBA to date (N = 43,110), which was highly heritable (h2 SNP GCTA = 41% [95% CI = 38-43%]) and had strong polygenic signal (LDSC h2 = 26%; mean χ2 = 1.23). The strongest association in our genome-wide association study (GWAS) implicated WNT16, a gene previously linked with neurodegenerative diseases such as Alzheimer, and Parkinson disease, and amyotrophic lateral sclerosis. This study is the first side-by-side evaluation of different computational approaches to estimate lifetime brain changes and their measurement characteristics. Careful assessment of methods for LBA computation had important implications for the interpretation of existing phenotypic and genetic results, and showed that relying on the residual method to estimate LBA from a single MRI scan captured brain shrinkage rather than current brain size. This makes this computationally-simple definition of LBA a strong candidate for more powerful analyses, promising accelerated genetic discoveries by maximising the use of available cross-sectional data.

INTRODUCTION: Polygenic Risk Scores for Alzheimer dementia (AD-PRS), a measure of aggregate AD genetic risk and polypharmacy have been associated with dementia. Here, we test their interaction's association with future dementia among older adults without baseline neurodegenerative diagnoses. METHODS: Using Cox proportional hazards and mortality-adjusted competing risk regression models we analysed up to 17.5 years all-cause incident dementia in the Lothian Birth Cohort 1936 (n=759, 105 dementia patients). We used polypharmacy (total or nervous-system-acting medications count), AD-PRS, and their interaction as main predictors. RESULTS: A non-significant interaction was found between AD-PRS and total polypharmacy (HR=1.06; p=0.15) or nervous-system-acting polypharmacy (HR=0.98; p=0.86) in shaping dementia risk. Omitting interaction, mortality-adjusted models showed significant AD-PRS prediction of dementia (HR ~1.40; p<0.001), non-significant total (HR=1.03; p=0.49), and nervous-system-acting polypharmacy effects (HR=1.27; p=0.069). DISCUSSION: Elucidating the complex interplay between polypharmacy and genetics could improve management of inappropriate medication in older adults genetically prone to dementia/AD.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson’s disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.