Biomedical subjects

Neil M Davies

Publications and source records attributed to Neil M Davies.

3 recordsLinked to original sources

PubMed2026

Family genetic designs in MoBa provide insights into health and functioning.

Genome-wide association studies using large, population-based samples of unrelated individuals have discovered thousands of genetic associations with health and disease1. These studies can help explain genetic and environmental risks. However, increasing evidence suggests that population-based estimates, while precise, can also reflect confounding that affects their use and interpretation. This confounding can be overcome using data from genotyped family members, such as nuclear mother-father-child trios2,3. However, samples of genotyped families are rare4-11. Here we illustrate some of the advantages of familial data using the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based cohort of parents and offspring with extensive genotype data (n ≈ 230,000) (ref. 3), along with broad and longitudinal phenotyping of health and functioning. We provide an overview of MoBa and describe the quality control of genotype data tailored to this extensively related sample. We then use trio data to illustrate how family-based genomic designs can identify distinct direct and indirect sources of genetic influence and structural confounding. As examples, we analyse children's height, educational achievement, depressive symptoms and sleep duration. These demonstrations highlight MoBa as a broadly valuable resource for advancing understanding of health and functioning across the lifecourse and generations.

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↗

PubMed2024

The importance of family-based sampling for biobanks.

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.

Humans↗