Simon R. White

Accelerating the domestication of a bioenergy crop: identifying and modelling morphological targets for sustainable yield increase in Miscanthus

To accelerate domestication of Miscanthus, an important energy crop, 244 replicated genotypes, including two different species and their hybrids, were analysed for morphological traits and biomass yield over three growing seasons following an establishment phase of 2 years in the largest Miscanthus diversity trial described to date. Stem and leaf traits were selected that contributed both directly and indirectly to total harvested biomass yield, and there was variation in all traits measured. Morphological diversity within the population was correlated with dry matter yield (DMY) both as individual traits and in combination, in order to determine the respective contributions of the traits to biomass accumulation and to identify breeding targets for yield improvement. Predictive morphometric analysis was possible at year 3 within Miscanthus sinensis genotypes but not between M. sinensis, Miscanthus sacchariflorus, and interspecific hybrids. Yield is a complex trait, and no single simple trait explained more than 33% of DMY, which varied from 1 to 5297g among genotypes within this trial. Associating simple traits increased the power of the morphological data to predict yield to 60%. Trait variety, in combination, enabled multiple ideotypes, thereby increasing the potential diversity of the crop for multiple growth locations and end uses. Both triploids and interspecific hybrids produced the highest mature yields, indicating that there is significant heterosis to be exploited within Miscanthus that might be overlooked in early selection screens within years 1–3. The potential for optimizing biomass yield by selecting on the basis of morphology is discussed.

Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution

Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential techniques cannot be used. In such settings, Bayesian inference can be performed using Approximate Bayesian Computation (ABC). However, in spite of many recent developments to ABC methodology, in many applications the computational cost of ABC necessitates the choice of summary statistics and tolerances that can potentially severely bias the estimate of the posterior.We propose a new “piecewise” ABC approach suitable for discretely observed Markov models that involves writing the posterior density of the parameters as a product of factors, each a function of only a subset of the data, and then using ABC within each factor. The approach has the advantage of side-stepping the need to choose a summary statistic and it enables a stringent tolerance to be set, making the posterior “less approximate”. We investigate two methods for estimating the posterior density based on ABC samples for each of the factors: the first is to use a Gaussian approximation for each factor, and the second is to use a kernel density estimate. Both methods have their merits. The Gaussian approximation is simple, fast, and probably adequate for many applications. On the other hand, using instead a kernel density estimate has the benefit of consistently estimating the true piecewise ABC posterior as the number of ABC samples tends to infinity. We illustrate the piecewise ABC approach with four examples; in each case, the approach offers fast and accurate inference.

Categorisation of built environment characteristics: the trouble with tertiles

BackgroundIn the analysis of the effect of built environment features on health, it is common for researchers to categorise built environment exposure variables based on arbitrary percentile cut-points, such as median or tertile splits. This arbitrary categorisation leads to a loss of information and a lack of comparability between studies since the choice of cut-point is based on the sample distribution.DiscussionIn this paper, we highlight the various drawbacks of adopting percentile categorisation of exposure variables. Using data from the SocioEconomic Status and Activity in Women (SESAW) study from Melbourne, Australia, we highlight alternative approaches which may be used instead of percentile categorisation in order to assess built environment effects on health. We discuss these approaches using an example which examines the association between the number of accessible supermarkets and body mass index.SummaryWe show that alternative approaches to percentile categorisation, such as transformations of the exposure variable or factorial polynomials, can be implemented easily using standard statistical software packages. These procedures utilise all of the available information available in the data, avoiding a loss of power as experienced when categorisation is adopted.We argue that researchers should retain all available information by using the continuous exposure, adopting transformations where necessary.

Drugs-Related Death Soon after Hospital-Discharge among Drug Treatment Clients in Scotland: Record Linkage, Validation, and Investigation of Risk-Factors

We validate that the 28 days after hospital-discharge are high-risk for drugs-related death (DRD) among drug users in Scotland and investigate key risk-factors for DRDs soon after hospital-discharge. Using data from an anonymous linkage of hospitalisation and death records to the Scottish Drugs Misuse Database (SDMD), including over 98,000 individuals registered for drug treatment during 1 April 1996 to 31 March 2010 with 705,538 person-years, 173,107 hospital-stays, and 2,523 DRDs. Time-at-risk of DRD was categorised as: during hospitalization, within 28 days, 29–90 days, 91 days–1 year, >1 year since most recent hospital discharge versus ‘never admitted’. Factors of interest were: having ever injected, misuse of alcohol, length of hospital-stay (0–1 versus 2+ days), and main discharge-diagnosis. We confirm SDMD clients’ high DRD-rate soon after hospital-discharge in 2006–2010. DRD-rate in the 28 days after hospital-discharge did not vary by length of hospital-stay but was significantly higher for clients who had ever-injected versus otherwise. Three leading discharge-diagnoses accounted for only 150/290 DRDs in the 28 days after hospital-discharge, but ever-injectors for 222/290. Hospital-discharge remains a period of increased DRD-vulnerability in 2006–2010, as in 1996–2006, especially for those with a history of injecting.

Assessing dynamic functional connectivity in heterogeneous samples

&NA; Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding‐window analysis, which aims to capture how the brains functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non‐dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k‐means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC. Graphical abstract Figure. No caption available. HighlightsEstimates of dFC may be affected by various sources of between‐people heterogeneity.We simulated fMRI data to investigate the effect of such heterogeneity on dFC.Differences in estimated dFC resulted from non‐dynamic features of the data.Choice of parameters in common methods produced spurious group differences in dFC.

Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan

Seminal human brain histology work has demonstrated developmental waves of myelination. Here, using a micro-structural magnetic resonance imaging (MRI) marker sensitive to myelin, we studied fine-grained age differences to deduce waves of growth, stability, and decline of cortical myelination over the life-cycle. In 484 participants, aged 8-85 years, we fitted smooth growth curves to T1- to T2-weighted ratio in each of 360 regions from one of 7 cytoarchitectonic classes. From the first derivatives of these generally inverted-U trajectories, we defined three milestones: the age at peak growth; the age at onset of a stable plateau; and the age at the onset of decline. Age at peak growth had a bimodal distribution comprising an early (pre-pubertal) wave of primary sensory and motor cortices and a later (post-pubertal) wave of association, insular and limbic cortices. Most regions reached stability in the 30s but there was a second wave reaching stability in the 50s. Age at onset of decline was also bimodal: in some right hemisphere regions, the curve declined from the 60s, but in other left hemisphere regions, there was no significant decline from the stable plateau. Network analysis of the micro-structural connectome revealed that late developing (second wave) regions had significantly higher degree and other measures of centrality. These results are consistent with regionally heterogeneous waves of intracortical myelinogenesis and age-related demyelination, which may be relevant to the onset of neuropsychiatric disorders, and dementia, and the staged acquisition and decline of motor and cognitive skills over the course of the life-cycle. Significance Statement Characterizing how the human cerebral cortex normally grows to maturity and then declines across the life-span could serve as a fundamental benchmark to identify aberrant developmental trajectories thought to underlie neuropsychiatric disorders emerging in young adults, and dementia in older people. We measured a magnetic resonance imaging marker linked to myelin in nearly 500 participants, aged 8-85 years. We showed how key milestones of growth, stability, and decline occur in waves, with generally primary motor and sensory regions constituting the early wave, and association regions the late wave. Later maturing regions had more connections to other regions. Our results suggest regionally heterogeneous waves of myelinogenesis and age-related demyelination, potentially related to the onset of neuropsychiatric disorders, and dementia.

Atlas-CNV: a validated approach to call Single-Exon CNVs in the eMERGESeq gene panel

Purpose: To provide a validated method to confidently identify exon-containing copy number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs. Methods: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples (midpool), when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap). Results: Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multi-exon and 29 single-exon CNVs with high C-scores were assessed by MLPA. Conclusions: Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We proposed guidelines and criteria to identify high confidence single-exon CNVs.

Biased sampling activity: an investigation to promote discussion: Biased sampling activity

Summary The statistical concept of sampling is often given little direct attention, typically reduced to the mantra “take a random sample”. This low resource and adaptable activity demonstrates sampling and explores issues that arise due to biased sampling.

May the odds be ever in your favour: May the odds be ever in your favour

Summary Probability and chance are essential concepts, not just in statistics but in real life. We present an adaptable activity which investigates what we mean by bias, how we can identify bias, and how we can use it to our advantage!

Sample size and classification error for Bayesian change-point models with unlabelled sub-groups and incomplete follow-up.

Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up. Through a simulation study, we consider the relationship of sample size to the estimates of the underlying shape, the existence of a change-point, and the classification-error of sub-group labels. We use a Bayesian framework to account for the missing labels, and the analysis of each simulation is performed using standard Markov chain Monte Carlo techniques. Our simulation study is inspired by cognitive decline as measured by the Mini-Mental State Examination, where our extended model is appropriate due to the commonly observed mixture of individuals within studies who do or do not exhibit accelerated decline. We find that even for studies of modest size (n = 500, with 50 individuals observed past the change-point) in the fixed effect setting, a change-point can be detected and reliably estimated across a range of observation-errors.