Biology subjects

Neale, M. C.

Publications and source records attributed to Neale, M. C..

3 recordsLinked to original sources

bioRxiv2018

Genetic risk for coronary heart disease alters the influence of Alzheimer’s genetic risk on mild cognitive impairment

BACKGROUNDAlzheimers disease (AD) is under considerable genetic influence. However, known susceptibility loci only explain a modest proportion of variance in disease outcomes. This small proportion could occur if the etiology of AD is heterogeneous. We previously found that an AD polygenic risk score (PRS) was significantly associated with mild cognitive impairment (MCI), an early stage of AD. Poor cardiovascular health is also associated with increased risk for AD and has been found to interact with AD pathology. Conditions such as coronary artery disease (CAD) are also heritable, and may contribute to heterogeneity if there are interactions of genetic risk for these conditions as there is phenotypically. However, case-control designs based on prevalent cases of a disease with relatively high case-fatality rate such as CAD may be biased toward individuals who have long post-event survival times and may therefore also identify loci with protective effects.\n\nMETHODSWe compared interactions between an AD-PRS and two CAD-PRSs, one based on a GWAS of incident cases and one on prevalent cases, on MCI status in 1,209 individuals.\n\nRESULTSAs expected, the incidence-based CAD-PRS interacts with the AD-PRS to further increase MCI risk. Conversely, higher prevalence-based CAD-PRSs reduced the effect of AD genetic risk on MCI status.\n\nCONCLUSIONSThese results demonstrate: i) the utility of including multiple PRSs and their interaction effects; ii) how genetic risk for one disease may modify the impact of genetic risk for another; and iii) the importance of considering ascertainment procedures of GWAS being used for genetic risk prediction.

genomics↗

bioRxiv2017

Using Structural Equation Modeling to Jointly Estimate Maternal and Foetal Effects on Birthweight in the UK Biobank

BackgroundTo date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individuals own genotype on their birthweight, their mothers genotype, or both.\n\nMethodsWe demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study.\n\nResultsUnlike simple regression models, our approach is unbiased when there is both a maternal and foetal effect. The method can be used when either the individuals own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype, and reasonable power to detect whether it is a foetal and/or maternal effect. We also identify a subset of birth weight associated SNPs that have opposing maternal and foetal effects in the UK Biobank.\n\nConclusionsOur results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.\n\nKey MessagesO_LIWe describe a structural equation model to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual.\nC_LIO_LIUsing simulation, we show that our approach is unbiased when there is both a maternal and foetal effect, unlike simple linear regression models. Additionally, we illustrate that the structural equation model is largely robust to measurement error and missing data for either the individuals own phenotype or the phenotype of their offspring.\nC_LIO_LIWe describe how the flexibility of the structural equation modelling framework will allow the inclusion of summary statistics from studies that are unable to share raw data.\nC_LIO_LIUsing the structural equation model to estimate the maternal and foetal effects of known birthweight associated loci in the UK Biobank, we identify three loci that have primary effects through the maternal genome and six loci that have opposite effects in the maternal and foetal genomes.\nC_LI

genetics↗

bioRxiv2017

Extending Causality Tests With Genetic Instruments: An Integration Of Mendelian Randomization And The Classical Twin Design

Mendelian Randomization (MR) is an important approach to modelling causality in non-experimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR.\n\nWe developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable.\n\nMR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MRs range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

genetics↗