Biology subjects

Schneider, K. A.

Publications and source records attributed to Schneider, K. A..

8 recordsLinked to original sources

bioRxiv2026

A Statistical Method to Estimate the Population-Level Frequencies of Plasmodium falciparum Haplotypes with Pfhrp2/3 Deletions in the Presence of Mixed-Clone Infections

BackgroundThe World Health Organization (WHO) has raised concerns over increasing Pfhrp2/3 deletions, undermining the sensitivity of Pfhrp2-based rapid diagnostic tests (RDTs). Close monitoring of the population and a change in diagnostic methods are recommended if the prevalence of parasites with Pfhrp2/3 deletions exceeds 5%. In high transmission settings, accurate estimates are hampered by the frequent occurrence of mixed-clone infections (multiplicity of infection; MOI). Objective and MethodsIf parasites with and without deletions are present in an infection, standard molecular assays cannot detect the presence of the former. To accurately estimate frequencies of haplotypes with Pfhrp2/3 deletions in the presence of mixed infections, a novel statistical model that combines genetic/molecular information from Pfhrp2/3 with that from neutral markers is introduced. Maximum-likelihood estimates (MLEs) are obtained for haplotype frequencies characterized by markers at Phrp2/3 loci and loci for neutral markers. The expectation-maximization algorithm is used to derive the MLEs. The adequacy of the method (precision and accuracy) is assessed by numerical simulations. ResultsThe method was applied to an active surveillance study conducted in a tribal community in Jagdalpur, India, which enrolled febrile community members (n = 432) between October and November 2021. Four markers each at Pfhrp2 and Pfhrp3 are combined with one marker each at Pfmsp1 (which encodes P. falciparum merozoite surface protein 1) and Pfmsp2. Data from a total of 117 patients who had both P. falciparum infections and genetic information for the molecular markers underwent further analysis with the novel statistical method. ConclusionResults indicate that this novel method has promising statistical properties (asymptotic and in finite samples) and can be readily applied to real-world situations. A stable implementation of the method in R is provided. This novel approach enables accurate estimation of Pfhrp2/3 deletion frequencies in complex P. falciparum infections, addressing a key limitation of current molecular surveillance methods. Author summaryPlasmodium falciparum (Pf) causes the most severe form of human malaria, accounting for over 90% of cases. Rapid diagnostic tests (RDTs) have become a cornerstone of malaria control. These RDTs detect Pf-specific antigens in a blood drop. HRP2/3 emerged as the best antigen for such tests because it is Pf-specific and expressed in abundance. However, some parasites lack the genes that code for HRP2/3 proteins. If parasites in an infection have such gene deletions, RDT results can be false negative. The WHO considers the containment of such deletions a public health priority and recommends monitoring their prevalence. The detection of HRP deletions is challenging if parasites with and without deletions co-occur in infections because standard molecular assays cannot detect deletions in this situation. To overcome this challenge, we introduce a novel statistical method to estimate the frequency distribution of parasite variants with deletions. The method combines information from neutral molecular markers and from HRP-related markers to correct for unobservable information. Here we provide a derivation of the statistical model, a stable implementation, and test its statistical properties with synthetic and real data, thereby showing that our method is well-suited for the underlying problem.

genetics↗

bioRxiv2025

Molecular surveillance of multiplicity of infection, haplotype frequencies, and prevalence in infectious diseases

BackgroundThe presence of multiple different pathogen variants within the same infection, referred to as multiplicity of infection (MOI), confounds molecular disease surveillance in diseases such as malaria. Specifically, if molecular/genetic assays yield unphased data, MOI causes ambiguity concerning pathogen haplotypes. Hence, statistical models are required to infer haplotype frequencies and MOI from ambiguous data. Such methods must apply to a general genetic architecture, when aiming to condition secondary analyses, e.g., population genetic measures such as heterozygosity or linkage disequilibrium, on the background of variants of interest, e.g., drug-resistance associated haplotypes. Methods and FindingsHere, a statistical method to estimate MOI and pathogen haplotype frequencies, assuming a general genetic architecture, is introduced. The statistical model is formulated and the relation between haplotype frequency, prevalence and MOI is explained. Because no closed solution exists for the maximum-likelihood estimate, the expectation-maximization (EM) algorithm is used to derive the maximum-likelihood estimate. The asymptotic variance of the estimator (inverse Fisher information) is derived. This yields a lower bound for the variance of the estimated model parameters (Cramer-Rao lower bound; CRLB). By numerical simulations, it is shown that the bias of the estimator decrease with sample size, and that its covariance is well approximated by the inverse Fisher information, suggesting that the estimator is asymptotically unbiased and efficient. Application of the method is exemplified by analyzing an empirical dataset from Cameroon concerning anti-malarial drug resistance. It is shown how the method can be utilized to derive population genetic measures associated with haplotypes of interest. ConclusionThe proposed method has desirable statistical properties and is adequate for handling molecular consisting of moderate number of multiallelic molecular markers. The EM-algorithm provides a stable iteration to numerically calculate the maximum-likelihood estimates. An efficient implementation of the algorithm alongside a detailed documentation is provided as supplementary material. Author summaryMalaria annually causes 263 million infections and 596,000 deaths. Control efforts are challenged by factors like spreading drug resistance. Monitoring pathogen variants at the genetic level (molecular surveillance), especially those linked to drug resistance, is a public health priority. A major challenge is the presence of multiple, genetically distinct pathogen variants (characterized by several genetic markers) within infections (multiplicity of infection). Because genetic assays do not provide phased information in this context, ambiguity in reconstructing the actual variants present in an infection arises. This challenge is not limited to malaria. Probabilistic methods are required to phase genetic data, i.e., to reconstruct the pathogen variants present in infections. As such, we introduce a statistical method to estimate the distribution of pathogen variants at the population level from unphased molecular data obtained from disease-positive specimens. This is a combinatorially difficult task, as the number of possible genetic variants grows exponentially with the amount of genetic information included. Although the method applies to data with an arbitrary genetic architecture, its application is constrained by computational limitations. The methods adequacy is explored and used to analyze a malaria dataset from Cameroon to guide applications. A stable numerical implementation is provided.

genetics↗

bioRxiv2023

Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance

Molecular/genetic methods are becoming increasingly important for surveillance of diseases like malaria. Such methods allow to monitor routes of disease transmission or the origin and spread of variants associated with drug resistance. A confounding factor in molecular disease surveillance is the presence of multiple distinct variants in the same infection (multiplicity of infection - MOI), which leads to ambiguity when reconstructing which pathogenic variants are present in an infection. Heuristic approaches often ignore ambiguous infections, which leads to biased results. To avoid such bias, we introduce a statistical framework to estimate haplotype frequencies alongside MOI from a pair of multi-allelic molecular markers. Estimates are based on maximum-likelihood using the expectation-maximization (EM)-algorithm. The estimates can be used as plug-ins to construct pairwise linkage disequilibrium (LD) maps. The finite-sample properties of the proposed method are studied by systematic numerical simulations. These reveal that the EM-algorithm is a numerically stable method in our case and that the proposed method is accurate (little bias) and precise (small variance) for a reasonable sample size. In fact, the results suggest that the estimator is asymptotically unbiased. Furthermore, the method is appropriate to estimate LD (by D', r2, Q*, or conditional asymmetric LD). Furthermore, as an illustration, we apply the new method to a previously-published dataset from Cameroon concerning sulfadoxine-pyrimethamine (SP) resistance. The results are in accordance with the SP drug pressure at the time and the observed spread of resistance in the country, yielding further evidence for the adequacy of the proposed method. The method is particularly useful for deriving LD maps from data with many ambiguous observations due to MOI. Importantly, the method per se is not restricted to malaria, but applicable to any disease with a similar transmission pattern. The method and several extensions are implemented in an easy-to-use R script. Author summaryAdvances in genetics render molecular disease surveillance increasingly popular. Unlike traditional incidence-based epidemiological data, genetic information provides fine-grained resolution, which allows monitoring and reconstructing routes of transmission, the spread of drug resistance, etc. Molecular surveillance is particularly popular in highly relevant diseases such as malaria. The presence of multiple distinct pathogenic variants within one infection, i.e., multiplicity of infection (MOI), is a confounding factor hampering the analysis of molecular data in the context of disease surveillance. Namely, due to MOI ambiguity concerning the pathogenic variants being present in mixed-clone infections arise. These are often disregarded by heuristic approaches to molecular disease surveillance and lead to biased results. To avoid such bias we introduce a method to estimate the distribution of MOI and frequencies of pathogenic variants based on a concise probabilistic model. The method is designed for two multi-allelic genetic markers, which is the appropriate genetic architecture to derive pairwise linkage-disequilibrium maps, which are informative on population structure or evolutionary processes, such as the spread of drug resistance. We validate the appropriateness of our method by numerical simulations and apply it to a malaria dataset from Cameroon, concerning sulfadoxine-pyrimethamine resistance, the drug used for intermittent preventive treatment during pregnancy.

genetics↗

bioRxiv2023

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data

BackgroundMolecular surveillance of infectious diseases allows the monitoring of pathogens beyond the granularity of traditional epidemiological approaches and is well-established for some of the most relevant infectious diseases such as malaria. The presence of genetically distinct pathogenic variants within an infection, referred to as multiplicity of infection (MOI) or complexity of infection (COI) is common in malaria and similar infectious diseases. It is an important metric that scales with transmission intensities, potentially affects the clinical pathogenesis, and a confounding factor when monitoring the frequency and prevalence of pathogenic variants. Several statistical methods exist to estimate MOI and the frequency distribution of pathogen variants. However, a common problem is the quality of the underlying molecular data. If molecular assays fail not randomly, it is likely to underestimate MOI and the prevalence of pathogen variants. Methods and findingsA statistical model is introduced which explicitly addresses data quality, by assuming a probability by which a pathogen variant remains undetected in a molecular assay. This is different from the assumption of missing at random, for which a molecular assay either performs perfectly or fails completely. The method is applicable to a single molecular marker and allows to estimate allele-frequency spectra, the distribution of MOI, and the probability of variants to remain undetected (incomplete information). Based on the statistical model, expressions for the prevalence of pathogen variants are derived and differences between frequency and prevalence are discussed. The usual desirable asymptotic properties of the maximum-likelihood estimator (MLE) are established by rewriting the model into an exponential family. The MLE has promising finite sample properties in terms of bias and variance. The covariance matrix of the estimator is close to the Cramer-Rao lower bound (inverse Fisher information). Importantly, the estimators variance is larger than that of a similar method which disregards incomplete information, but its bias is smaller. ConclusionsAlthough the model introduced here has convenient properties, in terms of the mean squared error it does not outperform a simple standard method that neglects missing information. Thus, the new method is recommendable only for data sets in which the molecular assays produced poor quality results. This will be particularly true if the model is extended to accommodate information from multiple molecular markers at the same time, and incomplete information at one or more markers leads to strong depletion of sample size.

molecular biology↗

bioRxiv2022

Evaluating Quantitative and Functional MRI As Potential Techniques to Identify the Subdivisions in the Human Lateral Geniculate Nucleus

Segmenting the magnocellular (M) and parvocellular (P) divisions of the human lateral geniculate nucleus (LGN) has been challenging yet remains an important goal because the LGN is the only place in the brain where these two information streams are spatially disjoint and can be studied independently. Previous research used the amplitude of responses to different types of stimuli to separate M and P regions (Denison et al., 2014; Zhang et al., 2015). However, this method is confounded because the hilum region of the LGN exhibits greater response amplitudes to all stimuli and can be mistaken for the M subdivision (DeSimone & Schneider, 2019). Therefore, we have employed two independent methodologies that do not rely upon the functional response properties of the M and P neurons to segment the M and P regions: 1) structural quantitative MRI (qMRI) at 3T to measure the T1 relaxation time, and 2) monocular and dichoptic functional MRI (fMRI) procedures to measure eye-specific responses. Our qMRI results agreed with the anatomical expectations, identifying M regions on the ventromedial surface of the LGN. The monocular fMRI procedure was better than the dichoptic condition to identify the eye-dominance signals. Both procedures revealed significant right eye bias, and neither could reliably identify the first M layer of the LGN. These findings indicated that the qMRI methods are promising whereas the functional identification of contralateral layers requires further refinement. HighlightsO_LIT1 parameter in qMRI segregates M and P regions of LGN in individual subjects at 3T. C_LIO_LIEye-specific voxels in LGN respond more strongly to monocular than dichoptic viewing. C_LIO_LIClusters of eye-specific regions but not layers can be separated at 1.5 mm resolution. C_LI

neuroscience↗

bioRxiv2022

The magnocellular division of the human medial geniculate nucleus preferentially responds to auditory transients

The medial geniculate nucleus (MGN), the auditory relay in the thalamus, is composed of three anatomical subdivisions: the ventral, dorsal and medial or magnocellular division. The functional differences among these nuclei have not been studied in humans, and in particular, the function of the magnocellular division is poorly understood in mammals in general. We anatomically segmented the MGN using proton-density-weighted magnetic resonance images (MRI) and measured the functional responses of the MGN to sustained and transient sounds, using functional MRI (fMRI). We observed that voxels in the ventromedial portion of the MGN, corresponding to the magnocellular division, exhibited a strong preference to transient sounds, whereas the remainder of the MGN showed no preference between sustained and transient sounds. We concluded that the magnocellular neurons in the MGN parallel the magnocellular neurons in its visual counterpart, the lateral geniculate nucleus (LGN), and constitute an information stream specialized for encoding stimuli dynamics. Significance statementThe medial geniculate nucleus is the auditory relay in the thalamus. It is composed of three anatomical divisions, of which the function of the magnocellular division is poorly understood. We show, using functional magnetic resonance imaging (fMRI) in humans, that the magnocellular neurons are strongly activated by transient auditory stimuli, similar to the magnocellular neurons in the lateral geniculate nucleus, the visual thalamic relay, which are rapidly adapting and specialized to encode visual transients. These results confirm that the auditory system represents stimuli using parallel information streams, employing similar encoding strategies as in other sensory modalities.

neuroscience↗

bioRxiv2022

Dyslexia linked to profound impairment in the magnocellular medial geniculate nucleus

The neurological basis of dyslexia, a common reading disorder, remains unclear but is hypothesized to be caused by either dysfunction of the magnocellular system in the brain, abnormal temporal processing, and/or deficient phonological skills. Using functional magnetic resonance imaging, we measured activity in the magnocellular portion of the medial geniculate nucleus, the auditory relay in the thalamus, and observed profoundly attenuated responses to non-linguistic transient but not sustained sounds in every subject with dyslexia we tested, compared to normal readers. Our finding unifies these three hypotheses and identifies a core deficit causing dyslexia.

neuroscience↗

bioRxiv2021

The impact of COVID-19 vaccination campaigns accounting for antibody-dependent enhancement

BackgroundCOVID-19 vaccines are approved, vaccination campaigns are launched, and worldwide return to normality seems within close reach. Nevertheless, concerns about the safety of COVID-19 vaccines arose, due to their fast emergency approval. In fact, the problem of antibody-dependent enhancement was raised in the context of COVID-19 vaccines. Methods and findingsWe introduce a complex extension of the model underlying the pandemic preparedness tool CovidSim 1.1 (http://covidsim.eu/) to optimize vaccination strategies with regard to the onset of campaigns, vaccination coverage, vaccination schedules, vaccination rates, and efficiency of vaccines. Vaccines are not assumed to immunize perfectly. Some individuals fail to immunize, some reach only partial immunity, and - importantly - some develop antibody-dependent enhancement, which increases the likelihood of developing symptomatic and severe episodes (associated with higher case fatality) upon infection. Only a fraction of the population will be vaccinated, reflecting vaccination hesitancy or contraindications. The model is intended to facilitate decision making by exploring ranges of parameters rather than to be fitted by empirical data. We parameterized the model to reflect the situation in Germany and predict increasing incidence (and prevalence) in early 2021 followed by a decline by summer. Assuming contact reductions (curfews, social distancing, etc.) to be lifted in summer, disease incidence will peak again. Fast vaccine deployment contributes to reduce disease incidence in the first quarter of 2021, and delay the epidemic outbreak after the summer season. Higher vaccination coverage results in a delayed and reduced epidemic peak. A coverage of 75% - 80% is necessary to prevent an epidemic peak without further drastic contact reductions. ConclusionsWith the vaccine becoming available, compliance with contact reductions is likely to fade. To prevent further economic damage from COVID-19, high levels of immunization need to be reached before next years flu season, and vaccination strategies and disease management need to be flexibly adjusted. The predictive model can serve as a refined decision support tool for COVID-19 management.

immunology↗