A maximum-likelihood method to estimate haplotype frequencies and prevalence alongside multiplicity of infection from SNP data

Obama, Henri Christian Junior Tsoungui; Schneider, Kristan A.

doi:10.3389/fepid.2022.943625

Cited by 7 publications

(16 citation statements)

References 55 publications

(93 reference statements)

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“…For the Poisson and conditional Poisson distribution, this approach was generalized to an arbitrary number of SNPs in cf. Li et al ( 30 ) and Tsoungui Obama and Schneider ( 31 ). In Schneider and Escalante ( 12 ) profile-likelihood confidence intervals for the MLE assuming a single locus and a conditional Poisson distribution are constructed.…”

Section: Resultsmentioning

confidence: 99%

The many definitions of multiplicity of infection

et al. 2022

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The presence of multiple genetically different pathogenic variants within the same individual host is common in infectious diseases. Although this is neglected in some diseases, it is well recognized in others like malaria, where it is typically referred to as multiplicity of infection (MOI) or complexity of infection (COI). In malaria, with the advent of molecular surveillance, data is increasingly being available with enough resolution to capture MOI and integrate it into molecular surveillance strategies. The distribution of MOI on the population level scales with transmission intensities, while MOI on the individual level is a confounding factor when monitoring haplotypes of particular interests, e.g., those associated with drug-resistance. Particularly, in high-transmission areas, MOI leads to a discrepancy between the likelihood of a haplotype being observed in an infection (prevalence) and its abundance in the pathogen population (frequency). Despite its importance, MOI is not universally defined. Competing definitions vary from verbal ones to those based on concise statistical frameworks. Heuristic approaches to MOI are popular, although they do not mine the full potential of available data and are typically biased, potentially leading to misinferences. We introduce a formal statistical framework and suggest a concise definition of MOI and its distribution on the host-population level. We show how it relates to alternative definitions such as the number of distinct haplotypes within an infection or the maximum number of alleles detectable across a set of genetic markers. It is shown how alternatives can be derived from the general framework. Different statistical methods to estimate the distribution of MOI and pathogenic variants at the population level are discussed. The estimates can be used as plug-ins to reconstruct the most probable MOI of an infection and set of infecting haplotypes in individual infections. Furthermore, the relation between prevalence of pathogenic variants and their frequency (relative abundance) in the pathogen population in the context of MOI is clarified, with particular regard to seasonality in transmission intensities. The framework introduced here helps to guide the correct interpretation of results emerging from different definitions of MOI. Especially, it excels comparisons between studies based on different analytical methods.

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Section: Resultsmentioning

confidence: 99%

The many definitions of multiplicity of infection

et al. 2022

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“…For the haplotype based test, we assume that the haplotype frequencies are estimated from the noisy allele frequencies by solving the regression model [ 35 ]. Other methods of haplotype frequencies may also be used, see for instance [ 36 ] that proposes an EM algorithm, or [ 37 ] for a maximum likelihood based approach. The variances of the estimated regression coefficients as well as of their sums are then used as additional components of variance.…”

Section: Resultsmentioning

confidence: 99%

Haplotype based testing for a better understanding of the selective architecture

Chen,

Pelizzola,

Futschik

2023

BMC Bioinformatics

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Background The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies. Results Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits. Conclusions Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

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“…Reference-free methods, on the other hand, are not scalable. They either only handle 2–4 strains per infection ( Zhu et al 2018 , Gabbassov et al 2021 ) or are limited to only a few SNP sites ( Tsoungui Obama and Schneider 2022 ). Furthermore, these algorithms focus on resolving mixed infections within individual hosts and do not effectively leverage population-level SNP-haplotypes and allele frequencies (AFs).…”

Section: Introductionmentioning

confidence: 99%

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

Ju,

Liu,

2024

Bioinformatics

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Motivation Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology. Results We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets. Availability and Implementation The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

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A maximum-likelihood method to estimate haplotype frequencies and prevalence alongside multiplicity of infection from SNP data

Cited by 7 publications

References 55 publications

The many definitions of multiplicity of infection

The many definitions of multiplicity of infection

Haplotype based testing for a better understanding of the selective architecture

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

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