Biology subjects

Holmes, C. C.

Publications and source records attributed to Holmes, C. C..

8 recordsLinked to original sources

bioRxiv2021

Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

genetics↗

bioRxiv2021

High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

genomics↗

bioRxiv2020

Age modulates genetic risk for multiple common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Recent work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the relationship between genetic risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic risk. The predominant pattern shows genetic risk factors having the greatest impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants although the magnitude and form of the decrease varies among diseases. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction. Author summaryThe genes we inherit from our parents influence our risk for almost all diseases, from cancer to severe infections. With the explosion of genomic technologies, we are now able to use an individuals genome to make useful predictions about future disease risk. However, recent work has shown that the predictive value of genetic information varies by context, including age, sex and ethnicity. In this paper we introduce, validate and apply new statistical methods for investigating the relationship between age and genetic risk. These methods allow us to ask questions such as whether risk is constant over time, precisely how risk changes over time and whether all genetic risk factors have similar age profiles. By applying the methods to data from the UK Biobank, a prospective study of 500,000 people, we show that there is a tendency for genetic risk to decline with increasing age. We consider a series of possible explanations for the observation and conclude that there must be processes acting that we are currently unaware of, such as distinct phases of life in which genetic risk manifests itself, or interactions between genes and the environment.

genetics↗

bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗

bioRxiv2019

Graphing and reporting heterogeneous treatment effects through reference classes

BackgroundExploration and modelling of individual treatment effects and treatment heterogeneity is an important aspect of precision medicine in randomized controlled trials (RCTs). The usual approach is to develop a predictive model for individual outcomes and then look for an interaction effect between treatment allocation and important patient covariates. However, such models are prone to overfitting and multiple testing, and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.\n\nMethodsWe show how reference classes derived from background information can be used to alleviate this problem through a two-stage approach where we first estimate a key aspect of heterogeneity in the trial population and then explore for an interaction with the treatment effect along this axis of variation. This bypasses the search for interactions, protecting against multiple testing, and allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This would typically be applied to multivariate models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. We show how local and tilting schemes based on ranking patients by baseline risk can be used as a general approach for exploring heterogeneity of treatment effect.\n\nResultsWe illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and show how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably for higher risk strata.

bioinformatics↗

bioRxiv2018

Machine learning for randomised controlled trials: identifying treatment effect heterogeneity with strict control of type I error

BackgroundIt is widely acknowledged that retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the increasing availability of multiple data modalities on subjects and the desire to learn all we can from trial participants motivates the inclusion of such analyses within RCTs. Coupled to this, widespread advances in AI and machine learning (ML) methods hold great potential to utilise such data to characterise subjects exhibiting heterogeneous treatment response.\n\nMethodsWe present new learning strategies for RCT ML discovery methods that ensure strict control of the false positive reporting rate at a pre-specified level. Our approach uses randomised data partitioning and statistical or ML based prediction on held-out data. This can test for both crossover and non-crossover TEH. The former is done via a two-sample hypothesis test measuring overall predictive performance of the ML method. The latter is done via stacking the ML predictors alongside a classical statistical model to formally test the added benefit of the ML algorithm. An adaptation of recent statistical theory allows for the construction of a valid aggregate p-value. This learning strategy is agnostic to the choice of ML method.\n\nResultsWe demonstrate our approach with a re-analysis of the SEAQUAMAT trial. We find no evidence for any crossover subgroup who would benefit from a change in treatment from the current standard-of-care, artesunate, but strong evidence for significant noncrossover TEH within the artesunate treatment group. We find that artesunate provides a differential benefit to patients with high numbers of circulating ring stage parasites.\n\nConclusionsOur ML approach combined with the use of computational notebooks and version control can improve the robustness and transparency of RCT exploratory analyses. The methods allow researchers to apply the latest ML techniques safe in the knowledge that any declared associations are statistically significant at a user defined level.

bioinformatics↗

bioRxiv2018

A general framework for predicting the transcriptomic consequences of non-coding variation

Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently derive continuous dense embeddings of genes for downstream applications, and identify putatively functional eQTLs that are missed by high-throughput experimental approaches.

genetics↗

bioRxiv2017

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.

bioinformatics↗