Author post: Immunosequencing reveals diagnostic signatures of chronic viral infection in T cell memory

This guest post is by William DeWitt on his preprint (with co-authors) “Immunosequencing reveals diagnostic signatures of chronic viral infection in T cell memory”.

This is a post about our preprint at biorXiv. Although it’s a paper on infectious disease and immunology, our colleague (and Haldane’s Sieve contributor) Bryan Howie suggested we might engage the community here, since we think there are interesting connections to standard GWAS methodology. I’ll start with a one-paragraph immunology primer, then summarize what we’ve been up to, and what’s next.

Cell-mediated adaptive immunity is effected by T cells, which recognize infection through interface of the T cell receptor (TCR) with foreign peptides presented on the surface of all nucleated cells by major histocompatibility complex (MHC). During development in the thymus, maturing T cells somatically generate genes encoding the TCR according to a random process of V(D)J recombination and are passed through selective barriers against weak MHC affinity (positive selection) and strong self-peptide affinity (negative selection). This results in a diverse repertoire of self-tolerant receptors from which to deploy specific responses to threats from a protean universe of evolving pathogens. Upon recognition of foreign antigen, a T cell proliferates, generating a subpopulation with identical-by-descent TCRs. This clonal selection mechanism of immunological memory implies that the TCR repertoire dynamically encodes an individual’s pathogen exposure history, and suggests that infection with a given pathogen could be recognized by identifying concomitant TCRs.


In this study, we identify signatures of viral infection in the TCR repertoire. With a cohort of 640 subjects, we performed high-throughput immunosequencing of rearranged TCR genes, and serostatus tests for cytomegalovirus (CMV) infection. We used an analysis approach similar to GWAS; among the ~85 million unique TCRs in these data, we tested for enrichment of specific TCRs among CMV seropositive subjects, identifying a set of CMV-associated TCRs. These were reduced to a single dimension by defining the pathogen memory burden as the fraction of an individual’s TCRs that are CMV-associated, revealing a powerful discriminator. A binary classifier trained against this quantity demonstrated high cross validation accuracy.


The binding of TCR to antigen is mediated by MHC, which is encoded by the highly polymorphic HLA loci. Thus, the affinity of a given TCR for a given antigen is modulated by HLA haplotype. HLA typing was performed for this cohort according to standard methods, and we investigated enrichment of specific HLA alleles among the subjects in which each CMV-associated TCR appeared. Most CMV-associated TCRs were found to have HLA restrictions, and none were associated with more than one allele in any locus.

There is substantial literature identifying TCRs that bind CMV antigen through low-throughput in vitro methods, including so-called public TCRs, which arise in many individuals. Most public CMV TCRs were present in our data, however most were not in our list of diagnostically useful CMV-associated TCRs. This is understood by considering that V(D)J recombination produces different TCRs with different probability. Public TCRs, having high recombination probability, will be repeatedly recombined in the repertoires of all subjects, regardless of CMV infection status; their presence is not diagnostic for CMV serostatus, even if they are CMV-avid. Conversely, CMV-avid TCRs with low recombination probability will be private to one subject (if present at all) in any cohort of reasonable size; their enrichment in CMV seropositive subjects is not detectable. CMV-avid TCRs with intermediate recombination probability recombine intermittently, residing transiently in the repertoires CMV naïve individuals and reliably proliferating upon CMV exposure; the presence of these TCRs in an immunosequencing sample is diagnostic for infection status.

It may be interesting to draw a comparison with GWAS, where selection drives disease-associated variants with high effect size to low population frequency, out of reach of the detection power of any study. In contrast, the V(D)J recombination machinery is constant across individuals, and CMV-avid TCRs appear to span a broad range of recombination probabilities from public to private. This includes plenty in an intermediate regime of what we might call diagnostic TCRs, which can be used to build powerful classifiers of disease status that aren’t blunted by suppression of the most relevant features.

We’ll be making the data from this study available online, constituting the largest publically accessible TCR immunosequencing data set. It’ll be fun to see what other groups do with it.

Some things we’re still working on:
• We did cross validation to assess diagnostic accuracy (yellow curve in the ROC figure), including recomputation of CMV-associated TCRs for each holdout. Results are encouraging, but a more convincing test will be to diagnose a separate cohort. We’re in the process of acquiring these data.
• MHC polymorphism suggests that thymic selection barriers censor different TCRs in individuals with different HLA haplotype. We’ve done preliminary work identifying TCRs that are associated with more common HLA alleles, indicating the possibility of HLA typing from immunosequencing data. Interestingly, this necessitates a two-tailed test due to modulation of both positive and negative selection.
• Our association analysis relies on the recurrence of TCRs with identical amino acid sequence across individuals, but we’d like to be able to define TCR motifs more loosely, so that we can detect enrichment without requiring identity. This necessitates a similarity metric in amino acid space that captures similarity in avidity. We have some ideas here, and are testing them out on some validation data. It’s definitely a tough one, but could substantially increase power in this sort of study.

Author post: Limits to adaptation in partially selfing species

This guest post is by Matthew Hartfield (@mathyhartfield) on his preprint (with Sylvain Glemin) “Limits to adaptation in partially selfing species”, available from bioRxiv here

Our paper “Limits to adaptation in partially selfing species” is now available from bioRxiv. This preprint is the result from a collaboration that has been sent back-and-forth across the Atlantic for well over a year, so we are pleased to see it online.

Haldane’s Sieve, after which this blog is named, is a theory pertaining to the role of dominance in adaptation, which was initially developed for outcrossing species and then shown to be absent in selfing species. When beneficial alleles initially appear in diploid individuals, they do so in heterozygote form (so only one of two alleles at the locus carry the advantageous type). Mathematically, these mutations have selective advantage 1 + hs where h is the degree of dominance, and s the selective advantage. Haldane’s Sieve states that recessive mutations (h 1/2), because selection is not efficient on heterozygotes if mutations are recessive. However, self-fertilising individuals are able to rapidly create homozygote forms of the mutant, increasing the efficacy of selection acting on them. Yet selfing also increases genetic drift, and hence the risk that these adaptations will go extinct by chance. Consequently, an extension of Haldane’s Sieve states that if the mutation is recessive (h 1/2).

This result holds for a single mutant in isolation. Yet mutants seldom act independently; they usually arise alongside other alleles in the genome, each of which has their own evolutionary outcomes. A known additional advantage of outcrossing is that, through recombining genomes from each parent, selected alleles can be moved from disadvantageous genomes to fitter backgrounds. For example, say an adaptive allele was present in a population, and a second adaptation arose at a nearby locus. If the second allele was not as strongly selected as the first, then it has to arise on the same genome as the initial adaptation. Otherwise it is likely to be lost as the less-fit genotype is replaced over time, a process known as selective interference. However, outcrossing can unite the two mutations into the same genome, so both can spread.

Despite these potential advantages of outcrossing, the effect of selective interference has not yet been investigated in the context of how facultative selfing influences the fixation of multiple beneficial alleles. Our model therefore aimed to determine how likely it is that secondary beneficial alleles can fix in the population, given an existing adaptation was already present, and reproduction involved a certain degree of self-fertilisation.

After working through the calculations, two subtle yet important twists on Haldane’s Sieve revealed themselves. First, due to the effects of selection interference, Haldane’s Sieve is likely to be reinforced in areas of low recombination. That is, recessive mutants are more likely to be lost in outcrossers (when compared to single-locus results), with similar losses for dominant mutations in self-fertilising organisms. Secondly, we also investigated a case where the second beneficial mutant could be reintroduced by recurrent mutation. In this case, selection interference can be very severe in selfers due to the lack of recombination. Hence some degree of outcrossing would be optimal to prevent these beneficial alleles from being repeatedly lost, even if they are recessive. In the most extreme case, complete outcrossing is best if secondary mutations only confer minor advantages.

In recent years, the role that selection interference plays in affecting mating system evolution is starting to become recognised. Our theoretical study is just one of many that elucidates how important outcrossing can be in augmenting the efficacy of selection. Our hope is that these studies will spur on further empirical work quantifying the rate of adaptation in species with different mating systems, to further unravel why species reproduce in vastly different ways.

Author post: Adaptive evolution is substantially impeded by Hill-Robertson interference in Drosophila

This guest post is by David Castellano and Adam Eyre-Walker on their preprint (with co-authors) Adaptive evolution is substantially impeded by Hill-Robertson interference in Drosophila.

Our paper “Adaptive evolution is substantially impeded by Hill-Robertson interference in Drosophila”, in which we investigate the role of both the rate of recombination and the mutation rate on the rate of adaptive amino acid substitutions, has been available at biorxiv ( since 27 June.

Population genetics theory predicts that the rate of adaptive evolution should depend upon the rate of recombination; genes with low rates of recombination will suffer from Hill-Robertson interference (HRi) in which selected mutations interfere with each other (see the figure below): a newly arising advantageous mutation may find itself in competition for fixation with another advantageous mutation at a linked locus on another chromosome in the population, or in linkage disequilibrium with deleterious mutations, which will reduce its probability of fixation if it can not recombine away from them.

A schematic HRi example among adaptive alleles (left) and among adaptive and deleterious alleles (right).

A schematic HRi example among adaptive alleles (left) and among adaptive and deleterious alleles (right).

Likewise, it is expected that genes with higher mutation rates will undergo more adaptive evolution than genes with low mutation rates. More interestingly an interaction between the rate of recombination and the rate of mutation is also expected; HRi should be more prevalent in genes with high mutation rates and low rates of recombination. No attempt has been done so far to quantify the overall impact of HRi on the rate of adaptive evolution for any given genome. In our paper we propose a way to quantify the number of adaptive substitutions lost due to HRi – approximately 27% of all adaptive mutations, which would go to fixation since the split of D. melanogaster – D. yakuba if there was free recombination, are lost due to HRi. Moreover, we are able to estimate how the fraction of lost adaptive amino acid substitutions to HRi depends on gene’s mutation rate. In agreement with our expectations, genes with high mutation rates lose a significantly higher proportion of adaptive substitutions than genes with low mutation rates (43% vs 11%, respectively).

An open question is to what extent HRi affects rates of adaptive evolution in other species. Moreover, the loss of adaptive substitutions to HRi can potentially tell us something important about the strength of selection acting on some advantageous mutations, since weakly selected mutations are those that are most likely to be affected by HRi. This will require further analysis and population genetic modeling, but in combination with other sources of information, for example, the dip in diversity around non-synonymous substitutions, the site frequency spectrum the high frequency variants that are left by selective sweeps it may be possible to infer much more about the DFE of advantageous mutations than previously thought.

It will be of great interest to do similar analyses to those performed here in other species.

Comments very welcome!
David and Adam

Author post: Coalescent times and patterns of genetic diversity in species with facultative sex

This guest post is by Matthew Hartfield (@mathyhartfield) on “Coalescent times and patterns of genetic diversity in species with facultative sex”.

Our paper “Coalescent times and patterns of genetic diversity in species with facultative sex”, in which we investigate the genealogies of facultative sexuals, is now available from the biorxiv.

Most evolutionary biologists are obsessed with sex. Explaining why organisms reproduce sexually by combining genetic material is a tough problem. The main issue lies with the fact that asexuality (reproduction via clonality) should be able to outcompete sexuals due to sheer weight of numbers. Various theories have been put forward to explain why sex is so widespread. The majority of these revolve around the idea that exchanging genetic material enables the fittest possible genotype to be created, while that of asexuals should degrade over time.

While such theories are ubiquitous, data to test them has been scarce. Recent years have seen a boom in exploring the evolution of sex experimentally using facultative sexual organisms: species that can switch between sexual and asexual reproduction. Such experiments have demonstrated how sexual reproduction can evolve when exposed to stressful environments, or when moving between environmentally different areas. Yet major questions remain regarding what the underlying genetic causes of these transitions are. In addition, there are plenty of organisms that undergo ‘cryptic’ sex, which cannot be observed directly but can with genomic sequence analyses.

Coalescent models are important for analysing genomic data. These tools determine the relationship between neutral markers, and hence make predictions on how genetic diversity is affected depending on environmental structuring, localised natural selection, or other effects. However, classic models cannot be applied to systems with partial asexuality, as they assume the population reproduces entirely sexually.

We worked on introducing partial rates of sex into these models. In the simplest case (one population with a fixed rate of sex), we recovered a classic prediction that extensive divergence between alleles at the same site arises. This phenomenon occurs since lack of sex keeps the two alleles distinct over evolutionary time; only a rare bout of sex has any chance of creating the segregation needed for them to be descended from the same allele.

A schematic of Allelic Sequence Divergence (ASD) in asexuals: Xs are distinct mutations at each neutral site.

A schematic of Allelic Sequence Divergence (ASD) in asexuals: Xs are distinct mutations at each neutral site.

After recovering this familiar result, we worked to extend coalescent theory in partial asexuals to include various other biological phenomena. Two effects we looked at were gene conversion, and heterogeneity in sex rates that change over time or space.

Gene conversion, where one DNA sequence replaces part of a homologous chromosome, is usually regarded as being of minor evolutionary importance. Yet numerous studies of facultative sexuals often observe it as a common force, especially in species not exhibiting allelic sequence divergence (ASD). Could the two be related? Excitingly, we found that low rates of gene conversion become important in organisms with low rates of sex. That is, once sex becomes so rare as to caused ASD, small rates of gene conversion can then reverse the process, homogenizing alleles again. Rather than having higher diversity than otherwise similar sexual populations as expected with ASD (in the absence of gene conversion), asexual populations will have less diversity than comparable sexual populations if gene conversion is not too low.

It is also known that many organisms change their rates of sex over time or location, which can be triggered by environmental cues or organismal stress. By investigating such variation in the rate of sex, the analysis elegantly shows how even a short burst to obligate sex (over tens of generations) is enough to jumble genomes in the population, hence giving the same outcome as long-term obligate sex. If rates of sex are also different in separate geographical locals, then these differences can be detected if there is little gene flow between regions. Otherwise, both areas display intermediate rates of sex.

Coalescent tools are popular since they can be used to simulate complex evolutionary outcomes, which are then tested against genomic data. We used the mathematical analyses to outline a coalescent algorithm to account for partial rates of sex, and predict genetic diversity, under numerous scenarios. The code is available online ( for others to use.

These are exciting times for population genetics and evolution, with cheaper sequencing costs making it possible to wade through the genomes of more individuals than before. Yet accurately exploring the genetic landscape requires the creation of mathematical tools that accounts for organismal life history. These results will provide the first of many buildings blocks to determine the effects of selection and the environment on the evolution of facultative sexuals. They might eventually reveal why sex is so prevalent in nature.

Author post: Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis

This guest post is by Zamin Iqbal [@ZaminIqbal] and Phelim Bradley [@Phelimb]

Our paper “Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis” has just appeared on the Biorxiv. We’re excited about it for a number of reasons.

The idea of using a graph of genetic variation as a reference, instead of a linear genome, has been discussed for some while, and in fact a previous biorxiv preprint of ours applying them to the MHC has just come out in Nature Genetics:

In this paper we apply those ideas to bacteria, where we let go of the linear coordinate system in order to handle plasmid-mediated genes. Our idea is simple – we want to see if genomic data can be used to predict antibiotic resistance in bacteria, and we explicitly want to build a general framework that will extend to many species, and handle mixed infections.

The paper does not deal with the issue of discovering mechanisms/mutations/genes which drive drug resistance – we take a set of geno-pheno rules as prerequisite, and then use a graph of resistance mutations and genes on different genetic backgrounds to detect presence of alleles and compare statistical models – is the population clonal susceptible, minor resistant or major resistant? Although it is accepted that minor alleles can sweep to fixation, in general there is neither consensus nor quantitative data on the correlation between allele frequency and in vitro phenotypic resistance or patient outcome (the latter obviously being much harder). At a practical level,in some cases a clinician might avoid a drug if they knew there was a 5%-frequency resistance allele, and in others they might increase the dose. Resistance is of course a quantitative trait, often measured in terms of the minimum concentration of a drug required to stop growth of a fixed inoculum – but commonly a threshold is drawn and samples are classified in a binary fashion.

A paper last year from some of us ( showed that a simple panel of SNPs and genes was enough to predict resistance with high sensitivity and specificity for S. aureus (where SNPs, indels, chromosomal genes and plasmid-mediated genes can all cause resistance) – once you discard all samples with any mixed strains. (Standard process is to take a patient sample and culture “overnight” (12-24 hours), thus removing almost all diversity and samples which show any morphological signs of diversity after culture are discarded or subcultured). By contrast, for M. tuberculosis (which causes TB), known resistance mutations explain a relatively low proportion of phenotypic resistance (~85%) for first-line drugs, and even less for 2nd line (I explain below what 1st/2nd line are). The Mtb population within-host is highly structured and multiple genotypes can evolve in different loci within the body, so it’s important to be able to deal with mixtures. Typical phenotyping relies on several weeks of solid culture (Mtb is slow growing), but mixtures are more able to survive this type of culture than in the case of S. aureus.

We show with simulations that we can use the graph to detect low frequency mutations and genes (no surprise), and that for S. aureus we make no minor calls for our validation set of ~500 blood-cultured samples (no surprise). Each sample is phenotyped with 2 standard lab methods, and where they disagree a higher quality test is used to arbitrate. This consensus allows us to estimate error rates both for our method (called Mykrobe predictor) and for the phenotypic tests. As a result we’re able to show not only that we do comparably with FDA requirements for a diagnostic, but also that we match or beat the common phenotypic tests.

On the other hand for TB, the story is much more complex and interesting. We analyse ~3500 genomes in total, split into ~1900 training samples and ~1600 for validation. For M. tuberculosis, a sample is classed as resistant if after some weeks of culturing under drug pressure, the number of surviving colonies is >1% of the number of colonies from a control strain treated identically – the number 1% is of course arbitrary (set down by Canetti in the 1960s I think), though it has been shown that phenotypic resistance does correlate with worse patient outcome. Sequencing on the other hand is done before the drug pressure, so we are fundamentally testing a different population, and we can’t simply mirror that 1% allele frequency expectation. This is what we use the 1900 training samples for – determining what frequency to set for our minor-resistant model. We ended up using 20%, and also found that there
was an appreciable amount of lower frequency resistance, which did not survive the 6-week drug-pressure susceptibility test, but which might cause resistance in a patient.

Mtb infections can last a long time, and despite their slow growth, the sheer number of bacilli in a host result in a vast in-host diversity. As a result, mono therapies fail, as resistant strains sweep to fixation – standard treatment is therefore with 4 “first-line” drugs, reducing the chance that any strain has enough mutations to resist them all. If the first-line drugs fail, or if the strain is known to be resistant, then it is necessary to fall back to more toxic and less effective second-line drugs. We found, somewhat to our surprise, that

1. Overall, minor alleles contribute very little to phenotypic resistance in first-line drugs, but they do make a significant contribution to second-line drugs, improving predictive power by >15%. This matches previous reports that patient samples had mixed R and S alleles for 2nd line drugs. This could have major public health consequences, as resistance to these drugs needs to be detected to distinguish MDR-TB (resistant to isoniazid, rifampicin) from XDR-TB (isoniazid, rifampicin + second-line), a major concern for the WHO.

2. Interestingly, a noticeable number of rifampicin false-positive calls were due to SNPs which confer resistance but have been shown to slow growth. Since the phenotyping test is intrinsically a measure of relative growth, these strains may be misclassified as susceptible – i.e. these are probably false-susceptible calls due to an artefact of the nature of the test. This has been reported before by the way.

Anyway – please check out the paper for details. We think this large-scale analysis of whether minor alleles contribute to in vitro phenotype, and whether they should be used for prediction is new and interesting both scientifically and in terms of translation. The bigger question is what the consequences are for patient outcome, and how to deal with in-host diversity, and for that we of course need data collection and sharing. We’ve spent a lot of time in the Oxford John Radcliffe Hospital working with clinicians, and trying to determine what information they really need from this kind of predictive test, and we’ve produced both Windows/Mac apps with very simple user-interfaces (drag-the-fastq on, and let it run) for them to use; we’ve also produced an Illumina Basespace app, currently submitted to Illumina for approval, which should enable automated cloud-use.

Our paper also has a whole bunch of work I’ve not mentioned here, where we needed to identify species, and detect contaminants – most interesting when common contaminants can contain the same resistance gene as the species under test.

Our software is up on github here
including some desktop apps and example fastq files so you can test it.

Comments very welcome!

Zam and Phelim

PS By the way, the 4 first-line drugs have different effectiveness in different body compartments – see this interesting paper for the modelling of the consequences:

Author post: Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing

This guest post is by William Wen on his preprint “Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing”, arXived here.

Our paper “Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing” has been published in the journal Biostatistics, the preprint is also updated on the arXiv. The paper discusses linear mixed models (LMM) and the models commonly used for (rare variants) SNP set testing in a unified Bayesian framework, where fixed and random effects are naturally treated as corresponding prior distributions. Based on this general Bayesian representation, we derive Bayes factors as our primary inference device and demonstrate their usage in solving problems of hypothesis testing (e.g., single SNP and SNP set testing) and variable selection (e.g., multiple SNP analysis) in genetic association analysis. Here, we take the opportunity to summarize our main findings for a general audience.

Agreement with Frequentist Inference

We are able to derive various forms of analytic approximations of the Bayes factor based on the unified Bayesian model, and we find that these analytic approximations are connected to the commonly used frequentist test statistics, namely the Wald statistic, score statistic in LMM and the variance component score statistic in SNP set testing.

In the case of LMM-based single SNP testing, we find that under a specific prior specification of genetic effects, the approximate Bayes factors become monotonic transformations of the Wald or score test statistics (hence their corresponding p-values) obtained from LMM. This connection is very similar to what is reported by Wakefield (2008) in the context of simple logistic regression. It should be noted the specific prior specification (following Wakefield (2008), we call it implicit p-value prior) essentially assumes a larger a priori effect for SNPs that are less informative (either due to a smaller sample size or minor allele frequency). Although, from the Bayesian point of view, there seems to be a lack of proper justification for such prior assumptions in general, we often note that the overall effect of the implicit p-value prior on the final inference may be negligible in practice, especially when the sample size is large (We demonstrate this point with numerical experiments in the paper).

For SNP set testing, we show that the variance component score statistic in the popular SKAT model (Wu et al. (2011)) is also monotonic to the approximate Bayes factor in our unified model if the prior effect size under the alternative scenario is assumed small. Interestingly, such prior assumption represents a “local” alternative scenario for which score tests are known to be most powerful.

The above connections are well expected: after all, the frequentist models and the Bayesian representation share the exact same likelihood functions. From the Bayesian point of view, the connections reveal the implicit prior assumptions in the Frequentist inference. These connections also provide a principled way to “translate” the relevant frequentist statistics/p-values into Bayes factors for fine mapping analysis using Bayesian hierarchical models as demonstrated by Maller et al. (2012) and Pickrell (2014).

Advantages of Bayesian Inference

Bayesian Model Averaging

Bayesian model averaging allows simultaneous modeling of multiple alternative scenarios which may not be nested or compatible with each other. One interesting example is in the application of rare variants SNP set testing, where two primary classes of competing models, burden model (assuming most rare causal variants in a SNP set are either consistently deleterious or consistently protective) and SKAT model (assuming the rare causal variants in a SNP set can have bi-directional effects), target complimentary alternative scenarios. In our Bayesian framework, we show that these two types of alternative models correspond to different prior specifications, and a Bayes factor jointly considering the two types of models can be trivially computed by model averaging. A frequentist approach, SKAT-O, proposed by Lee et al. (2012) achieves the similar goal by using a mixture kernel (or prior from the Bayesian perspective). We discuss the subtle theoretical difference between the Bayesian model averaging and the use of SKAT-O prior and show by simulations, the two approaches have similar performance. Moreover, we find that the Bayesian model averaging is more general and flexible. To this end, we demonstrate a Bayesian SNP set testing example where three categories of alternative scenarios are jointly considered: in addition to the two aforementioned rare SNP association models, a common SNP association model is also included for averaging. Such application can be useful for eQTL studies to identify genes harboring cis-eQTLs.

Prior Specification for Genetic Effects

The explicit specification of the prior distributions on genetic effects for alternative models is seemingly a distinct feature of Bayesian inference. However, as we have shown, even the most commonly applied frequentist test statistics can be viewed as resulting from some implicit Bayesian priors. Therefore, it is only natural to regard the prior specification as an integrative component in modeling alternative scenarios. Many authors have shown that it is effective to incorporate functional annotations into genetic association analysis through prior specifications. In addition, we also show that in many practical settings, the desired priors can be sufficiently “learned” from data facilitated by Bayes factors.

Multiple SNP Association Analysis

Built upon the Bayes factor results, we demonstrate an example of multiple SNP fine mapping analysis via Bayesian variable selection in the context of LMM. The advantages of Bayesian variable selection and its comparison to the popular conditional analysis approach have been thoroughly discussed in another recent paper of ours (Wen et al. 2015).

Take-home Message

If single SNP/SNP set association testing is the end point of the analysis, the Bayesian and the commonly applied frequentist approaches yield similar results with very little practical difference. However, going beyond the simple hypothesis testing in genetic association analysis, we believe that the Bayesian approaches possess many unique advantages and is conceptually simple to apply in rather complicated practical settings.

The software/scripts, simulated and real data sets used in the paper are publicly available at github.


1. Wakefield, J. (2009). Bayes factors for genome‐wide association studies: comparison with P‐values. Genetic epidemiology, 33(1), 79-86.
2. Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011). Rare-variant association testing for sequencing data with the sequence kernel association test. The American Journal of Human Genetics, 89(1), 82-93.
3. Maller, J. B., McVean, G., Byrnes, J., Vukcevic, D., Palin, K., Su, Z., Wellcome Trust Case Control Consortium., et al. (2012). Bayesian refinement of association signals for 14 loci in 3 common diseases. Nature genetics, 44(12), 1294-1301.
4. Pickrell, J. K. (2014). Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. The American Journal of Human Genetics, 94(4), 559-573.
5. Lee, S., Wu, M. C., & Lin, X. (2012). Optimal tests for rare variant effects in sequencing association studies. Biostatistics, 13(4), 762-775.
6. Wen, X., Luca, F., & Pique-Regi, R. (2014). Cross-population meta-analysis of eQTLs: fine mapping and functional study. bioRxiv, 008797.

Author post: Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance

This guest post is by Pleuni Pennings about her paper (with co-authors) Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance, bioRxived here. This is a cross-post from Pleuni’s blog.

Almost three years ago, in early 2012, I attended a talk by Martin Nowak. He talked about cancer and one of the things he said was that treatment with multiple drugs at the same time is a good idea because it helps prevent the evolution of drug resistance. Specifically, he explained, when treatment is with multiple drugs, the pathogen (tumor cells in the case of cancer) needs to acquire multiple resistance mutations at the same time in order to escape drug pressure.

As I listened to Martin Nowak’s talk, I was thinking of HIV, not cancer. At that time, I had already spent about two years working on drug resistance in HIV. Treatment of HIV is always with multiple drugs, for the same reason that Martin Nowak highlighted in his talk: it helps prevent the evolution of drug resistance.

However, as I read the HIV drug resistance literature and analyzed sequence data from HIV patients, I found evidence that drug resistance mutations in HIV tend to accumulate one at a time. This is contrary to the generally accepted idea that the pathogen must acquire resistance mutations simultaneously.

There seemed to be a clear mismatch between data and theory. Data show mutations are acquired one at a time, and theory says mutations must be acquired simultaneously. One of the two must be wrong, and it can’t be the data![1]


After Martin Nowak’s talk, I went up to him and told him how I thought data didn’t fit the theory. Martin’s response: “Oh, that is interesting!” (Imagine this being said with an Austrian accent). “Let’s meet and talk about it.”

So, we met. Logically, Alison Hill and Daniel Rosenbloom, then grad students in Martin’s group, were there too. I had already met with Alison and Daniel many times, since they were also working on drug resistance in HIV. John Wakeley (my advisor at Harvard) came to the meeting too.

Between the five of us, we brainstormed and fairly quickly realized that one solution to the conundrum was to assume that a body’s patient consisted of different compartments and that each drug may not penetrate into each compartment. Maybe we found this solution quickly because Alison and Daniel had already been thinking of the issue of drug penetration in the context of another project. A body compartment that has only one drug instead of two or three would allow a pathogen that has acquired one drug resistance mutation to replicate. If a pathogen with just one mutation has a place to replicate, this makes it possible for the pathogen to acquire resistance mutations one at a time.

We decided to start a collaboration to analyze a formal model to see whether our intuition was correct. Over the following three years, there were some personnel changes and several moves, graduations and new jobs. Stefany Moreno joined the project as a student from the European MEME Master’s program when she spent a semester in Martin’s group. When I moved to Stanford, Dmitri Petrov became involved in the project. Next, Alison and Daniel each got their PhD and started postdocs (Alison at Harvard, Daniel at Columbia), Stefany got her MSc and started a PhD in Groningen, I had a baby and became an assistant professor at SFSU. No one would have been surprised if the project would never have been finished! But we stuck with it and after many hours of work, especially by the first authors Alison and Stefany, and uncountable Google Hangout meetings, we can now confidently say that our initial intuition from that meeting in 2012 was correct. Compartments with imperfect drug penetration indeed allow pathogens to acquire drug resistance one mutation at a time. And, importantly, the evolution of multi-drug resistance can happen fast if mutations can be acquired one at a time, much faster than when simultaneous mutations are needed.

Our manuscript can be found on the BioRxiv (link). It is entitled “Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance.” We hope you find it useful!

[1]Of course, it could be my interpretation of the data!