Testing for dependence on tree structures

Behr, Merle; Ansari, Azim; Munk, Axel; Holmes, Chris

doi:10.1073/pnas.1912957117

Cited by 18 publications

(15 citation statements)

References 20 publications

(34 reference statements)

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“…It would be interesting to compare results from a distance dependent CRP with the tree-informed CRP that we have defined here. Yet another alternative is to identify a MAP set of branch mutations b or some other high-confidence set of mutated branches, as in Behr et al (2020) . This has the advantage of avoiding both computational bottlenecks (computing the tree-informed prior and sampling from the posterior), but the method described in Behr et al (2020) does not account for the covariance between individuals induced by combinations of haplotypes and additive effects.…”

Section: Discussionmentioning

confidence: 99%

“…Ewens’s sampling formula provides an intuitive mechanism for introducing prior information about haplotype relatedness: assuming that the phylogenetic tree of the haplotypes is known rather than random. This defines a prior distribution over the allelic series that is informed by a tree,

In this way, our approach is similar to other models that include phylogenetic information; for example, by modeling distributional “changepoints” on a tree ( Ansari and Didelot 2016 ), or by using phylogenetic distance as an input for a distance-dependent CRP ( Cybis et al 2018 ), among others ( Zhang et al 2012 ; Thompson and Kubatko 2013 ; Behr et al 2020 ; Selle et al 2020 ). In particular, Ansari and Didelot (2016) specify a prior distribution over the allelic series by defining the prior probability that each branch of a tree is functionally mutated with respect to a phenotype (in their case, a categorical trait).…”

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confidence: 99%

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Inferring the Allelic Series at QTL in Multiparental Populations

2020

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Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully-Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Inferring the Allelic Series at QTL in Multiparental Populations

2020

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“…We note that recent work provides a method that can test for a dependence between the tree structure and a given leaf variable (e.g. isolate phenotype coded as a binary or continuous variable) [ 22 ]. This explicitly takes into account all possible clustering structures and avoids having to arbitrarily choose a cut-off threshold.…”

Section: Methodsmentioning

confidence: 99%

“…For this reason, it is important to note this sensitivity when superimposing meta-data onto a tree structure. This issue can be avoided with the use of a formal statistical test between the tree structure and meta-data as proposed in [22]. Isolates are plotted along the first two principal components.…”

Section: Plos Geneticsmentioning

confidence: 99%

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

et al. 2020

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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.

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“…For this reason, it is important to note this sensitivity when superimposing meta-data onto a tree structure. It is possible to avoid this issue by using a formal statistical test between the tree structure and meta-data as proposed in [22].…”

Section: Sensitivity Of Hac-based Discrete Clusteringmentioning

confidence: 99%

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

Watson

Taylor

Ashley

et al. 2020

Preprint

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1Genetic surveillance of malaria parasites supports malaria control programmes, treatment 2 guidelines and elimination strategies. Surveillance studies often pose questions about malaria 3 parasite ancestry (e.g. about the spread of antimalarial resistance) and employ methods that 4 characterise parasite population structure. Many of the methods used to characterise struc-5 ture are algorithms developed in machine learning (ML) and depend on a genetic distance 6 matrix, e.g. principal coordinates analysis (PCoA) and hierarchical agglomerative clustering 7 (HAC). However, PCoA and HAC are sensitive to both the definition of genetic distance and 8 algorithmic specification. Importantly, neither algorithm generates an inferred malaria para-9 site ancestry. As such, PCoA and HAC can support (e.g. via exploratory visualisation and 10 hypothesis generation), but not answer comprehensively, key questions about malaria para-11 site ancestry. We illustrate the sensitivity of PCoA and HAC using 393 P. falciparum whole 12 genome sequences collected from Cambodia and neighbouring regions (where antimalarial re-13 sistance has emerged and spread recently) and we provide tentative guidance for the use of 14 PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for 15 fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scien-16 tific question; (ii) clear justification of methods used along with discussion of any inferential 17 limitations; (iii) publicly available genetic distance matrices; and (iv) sensitivity analyses. To 18 bridge the inferential disconnect between the output of the ML algorithms and the scientific 19 questions of interest, tailor-made statistical models are needed to infer malaria parasite an-20 cestry. In the absence of such models speculative reasoning should feature only as discussion 21 but not as results. 22 24 ular surveillance to understand how the parasites causing malaria are spreading, to track drug 25 resistance and identify its emergence, and to understand where to target interventions. Malaria 26 parasite genetic data have been accrued increasingly rapidly in recent years, but the analytical 27 methods for making sense of them lag behind. In particular, the methods used currently to in-28 fer malaria parasite ancestry, which is often central to the questions posed by studies of malaria 29 parasite genetic epidemiology, have significant limitations. An important goal in malaria parasite 30 genetic epidemiology is inference of the full ancestral recombination graph. However, there are no 31 scalable methods directly applicable to malaria parasites. Instead, the first step towards inferring 32 1 ancestry involves characterisation of contemporary population genetic structure using computa-33 tionally tractable methods. Many questions of clinical and public health relevance, for example, 34 interpreting reduced haplotype diversity as a selective sweep, rely on methods that first characterise 35 the underlying population structure w...

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Testing for dependence on tree structures

Cited by 18 publications

References 20 publications

Inferring the Allelic Series at QTL in Multiparental Populations

Inferring the Allelic Series at QTL in Multiparental Populations

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

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

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