Biomedical subjects

George Davey Smith

Publications and source records attributed to George Davey Smith.

5 recordsLinked to original sources

PubMed2026

Robust inference and correlates from genetic associations with personality.

Personality traits describe stable differences in how people think, feel and behave, and how they interact with and experience their social and physical environments1,2. Many questions remain unanswered about associations between DNA and personality traits, such as their robustness, their generalizability and the biological and social pathways through which they act. Here we meta-analyse data across 46 cohorts comprising 611,037 to 1.14 million participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience), and data from up to 50,725 participants for within-family GWAS. We identify 1,260 lead genetic variants associated with personality, including 824 novel variants3. Common genetic variants explain a moderate 4.8-9.3% of the variance in measures of each trait, and 9.3-13.3% among instruments with typical measurement reliability. Genetic associations with personality are highly consistent but not identical across geography, reporter (self versus close other), age group and measurement instrument, and we find minimal spousal assortment for personality in recent history. In contrast to many other social and behavioural traits4,5, within-family GWAS and polygenic index analyses indicate that genetic associations with personality are minimally confounded by the shared family environment. Polygenic prediction, genetic correlation and Mendelian randomization analyses indicate that personality traits have widespread, potentially causal associations with consequential behaviours and life outcomes. Overall, we find that the genetic architecture of personality is robustly generalizable, minimally confounded and widely relevant to human experience.

Journal Article↗

PubMed2026

Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study.

BACKGROUND: Neurodegeneration in Alzheimer's disease (AD) is thought to be driven by amyloid-beta and tau deposition in the cerebral vasculature and brain. As the eye is an extension of the central nervous system, this study aimed to determine which neurovascular and neuroretinal changes in the eye are caused by AD rather than associations of the disease. METHODS: Bidirectional two-sample univariable and multivariable Mendelian randomization (MR) methods were applied. Instrumental variables were derived from genome-wide association studies (GWAS) of AD and the following ocular features: thickness measurements of central macula (MT), retinal nerve fibre layer (mRNFL), ganglion cell-inner plexiform layer (mGCIPL), outer nuclear layer (ONL), inner segment layer (IS), and outer segment (OS) from macular region OCT scans; arteriolar tortuosity (AT), venular tortuosity (VT), venular width (VW), fractal dimension (FD), vertical cup-to-disc ratio (VCDR), optic cup area (OCA), and optic disc area (ODA) derived from other imaging methods. RESULTS: There was strong evidence that genetic liability to AD affected the retinal vasculature by specifically increasing AT (β = 0.007;95%CI=0.002,0.011;p-value=0.005) in UK Biobank participants (n=52,798). AD may influence the mRNFL (β=-0.047,95%CI=-0.119,0.023,p-value=0.18) and mGCIPL (β=-0.061;95%CI=-0.14,0.025,p-value=0.16) of the inner retina and OS layer (β = 0.044;95%CI=-0.0001,0.08;p-value=0.05) but the evidence was weak. Multivariable MR analysis showed that a causal relationship between optic disc area and AD (OR=0.76;95%CI=0.62,0.93,p-value=0.009) was probably mediated by refractive error. CONCLUSION: Early cerebrovascular signs of AD may be detected by examination of the eye. Further investigation is required to determine the clinical utility of eye screening for dementia.

Humans↗

PubMed2025

No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.

Mendelian randomization (MR) is increasingly used in epidemiological studies to investigate causal relationships. MR depends on 3 fundamental instrumental variable assumptions: relevance, independence, and exclusion restriction. Studies often assume that MR mitigates bias from confounding due to the random allocation of genetic variants at conception. In this perspective, using causal directed acyclic graphs, we discuss several scenarios where biases in MR analyses may arise due to the nature of the data or methods being used. These include (1) collider bias due to the nonrandom selection of participants into study populations used for conducting genome-wide association studies (GWAS), (2) indirect genetic effects arising from population-based GWAS rather than within-family studies, and (3) collider bias due to gene-environment interaction effects on the exposure in nonlinear MR analyses. We provide practical considerations for examining and reducing these biases in MR analyses.

Humans↗

PubMed2025

Genome-wide association study of long COVID.

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

Humans↗

PubMed2016

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

Atlases as Topic↗