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

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

BACKGROUND

Molecular/genetic methods are becoming increasingly important for surveillance of diseases like malaria. Such methods allow monitoring 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.

METHODS

To avoid 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 [Formula: see text], [Formula: see text], [Formula: see text], 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.

CONCLUSION

The proposed method can be readily applied in practice for malaria disease surveillance as a replacement for heuristic methods. The first benefit is its ability to estimate MOI, which scales with transmission intensities, and, in a temporal context, can be used to evaluate the effectiveness of disease control measures. MOI is best estimated from molecular markers that are not under selection (neutral markers) and exhibit sufficient genetic variation. The second advantage is that it can estimate pairwise LD without deflating sample size as in heuristic methods, thereby limiting uncertainty in the estimates. This is particularly useful when 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.

Review of MrsFreqPhase methods: methods designed to estimate statistically malaria parasite multiplicity of infection, relatedness, frequency and phase

Malaria parasites are haploid within humans, but infections often contain genetically distinct groups of clonal parasites. When the per-infection number of genetically distinct clones (i.e., the multiplicity of infection, MOI) exceeds one, and per-infection genetic data are generated in bulk, important information are obfuscated. For example, the MOI, the phases of the haploid genotypes of genetically distinct clones (i.e., how the alleles concatenate into sequences), and their frequencies. This complicates many downstream analyses, including relatedness estimation. MOIs, parasite sequences, their frequencies, and degrees of relatedness are used ubiquitously in malaria studies: for example, to monitor anti-malarial drug resistance and to track changes in transmission. In this article, MrsFreqPhase methods designed to estimate statistically malaria parasite MOI, relatedness, frequency and phase are reviewed. An overview, a historical account of the literature, and a statistical description of contemporary software is provided for each method class. The article ends with a look towards future method development, needed to make best use of new data types generated by cutting-edge malaria studies reliant on MrsFreqPhase methods.

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

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.

Low Prevalence of Antimalarial Resistance Mutations in India During 2014-2015: Impact of Combining First-line Therapy With Primaquine

BACKGROUND

Antimalarial drug resistance surveillance and containment are crucial for countries aiming to eliminate malaria. Monitoring resistance evolution through studies before and after treatment policy changes is essential.

METHODS

A total of 939 Plasmodium falciparum-positive blood samples were collected between 2014 and 2015 across 10 sites in India, categorized into 4 geographic clusters. Polymerase chain reaction-amplified products were sequenced to identify point mutations at drug resistance-conferring genes (Pfdhfr, Pfdhps, Pfmdr1, and Pfk13).

RESULTS

Triple Pfdhfr mutants were found only in northeast India bordering Myanmar, while the wild type (WT) was dominant in central India. Pfdhps WTs were prevalent in all areas, and no double mutants were found. Except in northwest India, Pfmdr1 WT was dominant in all clusters. Nonsynonymous double mutations were only found in northwest India. Only synonymous mutations occurred in Pfk13. These were found in central India at a low frequency. The pattern of linkage disequilibrium and principal component analysis reflects low pressure for drug resistance and heterogeneity between the geographic clusters.

CONCLUSIONS

Resistance levels were highest in northeast India, close to the Myanmar border, where resistance is common. Primaquine has been widely used as a gametocidal and schizonticidal drug, likely contributing to maintaining low drug resistance levels and preventing strong selection for resistance.

Haplotype based testing for a better understanding of the selective architecture

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.

The many definitions of multiplicity of infection

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.