BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization.

This study focused on resolving the relationship between BMI and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on type 2 diabetes; however, the properties of these associations and their validity as genetic instruments need to be considered alongside established and new methods for undertaking Mendelian randomization (MR). We explore the potential for pleiotropic genetic variants to generate bias, revise existing estimates, and illustrate value in new analysis methods. A two-sample MR approach with 96 genetic variants was used with three different analysis methods, two of which (MR-Egger and the weighted median) have been developed specifically to address problems of invalid instrumental variables. We estimate an odds ratio for type 2 diabetes per unit increase in BMI (kg/m(2)) of between 1.19 and 1.38, with the most stable estimate using all instruments and a weighted median approach (1.26 [95% CI 1.17, 1.34]). TCF7L2(rs7903146) was identified as a complex effect or pleiotropic instrument, and removal of this variant resulted in convergence of causal effect estimates from different causal analysis methods. This indicated the potential for pleiotropy to affect estimates and differences in performance of alternative analytical methods. In a real type 2 diabetes-focused example, this study demonstrates the potential impact of invalid instruments on causal effect estimates and the potential for new approaches to mitigate the bias caused.Medical Research Council (Grant IDs: MC_UU_12013/1, MC_UU_12013/2, MC_UU_12013/3); University of Bristol; Wellcome Trust (Grant ID: 100114); Medical Research Council (Methodology Research Fellowship, Grant ID: MR/N501906/1); Cancer Research UK (C18281/A19169).This is the author accepted manuscript. The final version is available from American Diabetes Association via http://dx.doi.org/10.2337/db16-041

A multivariant recall-by-genotype study of the metabolomic signature of BMI

OBJECTIVE: This study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design. METHODS: A recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites. RESULTS: After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high‐BMI GRS group (β = −0.32, 95% CI: −0.46 to −0.18, Benjamini‐Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet‐related metabolites, including hippurate, which had lower mean abundance in individuals in the high‐BMI GRS group (β = −0.29, 95% CI: −0.44 to −0.15, Benjamini‐Hochberg adjusted p = 0.008). CONCLUSIONS: Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging

Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

Population structure can bias the results of genetic and epidemiological analysis. Here, Haworth et al. report that fine-scale structure is detectable in apparently homogeneous samples such as ALSPAC when measured very precisely, and remains detectable in UK Biobank despite conventional approaches to account for it

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference.

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner

Spatiotemporal Rank Filtering Improves Image Quality Compared to Frame Averaging in 2-Photon Laser Scanning Microscopy.

Live imaging of biological specimens using optical microscopy is limited by tradeoffs between spatial and temporal resolution, depth into intact samples, and phototoxicity. Two-photon laser scanning microscopy (2P-LSM), the gold standard for imaging turbid samples in vivo, has conventionally constructed images with sufficient signal-to-noise ratio (SNR) generated by sequential raster scans of the focal plane and temporal integration of the collected signals. Here, we describe spatiotemporal rank filtering, a nonlinear alternative to temporal integration, which makes more efficient use of collected photons by selectively reducing noise in 2P-LSM images during acquisition. This results in much higher SNR while preserving image edges and fine details. Practically, this allows for at least a four fold decrease in collection times, a substantial improvement for time-course imaging in biological systems

XCR1 Venus Dendritic cell in explanted lymph node

60 frame time series of XCR1 Venus Dendritic Cell imaged within explanted lymph node using Nikon A1R. Each frame corresponds to a single raster sca

PS-Speck Beads + Signal and noise masks

1000 frame time series of Yellow PS-Speck beads with each frame corresponding to a single raster scan with low excitation power; Also, signal and noise mask images generated from thresholding a gaussian filtered averaged image of all 1000 frame

Stack of VPD 450 T cells in Lymph node

82 Z slices, 30 time points, with each time point corresponding to a single raster scan of the sample. Created in Image