Inferring whole-genome histories in large population datasets

Kelleher, Jerome; Wong, Yan; Wohns, Anthony Wilder; Fadil, Chaimaa; Albers, Patrick K.; McVean, Gil

doi:10.1038/s41588-019-0483-y

Cited by 233 publications

(398 citation statements)

References 64 publications

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“…We do not know the true genealogies underlying real data, but recent methods are available to estimate them at scale [Kelleher et al, 2019, Speidel et al, 2019. In Figure 3, we showed that Branch and Site statistics matched well in simulated data.…”

Section: Application To 1000 Genomes Tree Sequencesmentioning

confidence: 90%

“…The methods were subsequently extended and refined for forwards-time simulations [Kelleher et al, 2018, Haller et al, 2018, with similarly large efficiency gains. Recent work has shown that tree sequence algorithms can also be used to massively increase the scalability of methods for inferring genome-wide genealogies, and making it possible to infer trees for millions of samples [Kelleher et al, 2019]. The key to the remarkable efficiency of tree sequence algorithms is the way that shared structure in adjacent trees along the genome is encoded.…”

Section: Introductionmentioning

confidence: 99%

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Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

Ralph¹,

Thornton

Kelleher

2019

Preprint

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AbstractAs a genetic mutation is passed down across generations, it distinguishes those genomes that have inherited it from those that have not, providing a glimpse of the genealogical tree relating the genomes to each other at that site. Statistical summaries of genetic variation therefore also describe the underlying genealogies. We use this correspondence to define a general framework that efficiently computes single-site population genetic statistics using the succinct tree sequence encoding of genealogies and genome sequence. The general approach accumulates “sample weights” within the genealogical tree at each position on the genome, which are then combined using a “summary function”; different statistics result from different choices of weight and function. Results can be reported in three ways: by site, which corresponds to statistics calculated as usual from genome sequence; by branch, which gives the expected value of the dual site statistic under the infinite-sites model of mutation, and by node, which summarizes the contribution of each ancestor to these statistics. We use the framework to implement many currently-defined statistics of genome sequence (making the statistics’ relationship to the underlying genealogical trees concrete and explicit), as well as the corresponding “branch” statistics of tree shape. We evaluate computational performance using simulated data, and show that calculating statistics from tree sequences using this general framework is several orders of magnitude more efficient than optimized matrix-based methods in terms of both run time and memory requirements. We also explore how well the duality between site and branch statistics holds in practice on trees inferred from the 1000 Genomes Project dataset, and discuss ways in which deviations may encode interesting biological signals.

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Section: Application To 1000 Genomes Tree Sequencesmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

Ralph¹,

Thornton

Kelleher

2019

Preprint

Self Cite

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“…To motivate the haplotype network model, we use the example from Kelleher et al (2019) that presents 5 haplotypes spanning 7 bialelic polymorphic sites ( Table 1). Note that the 5 haplotypes are just a sample of the 2 7 = 128 possible haplotypes over the 7 sites.…”

Section: Motivating Examplementioning

confidence: 99%

“…As mentioned in the introduction, modeling phenotypic variation as a function of haplotype variation has extensive literature (Templeton et al, 1987;Balding, 2006;Thompson, 2013;Morris and Cardon, 2019). The prime motivation for this work is the recent growth in the generation of large scale genomic datasets and methods to build phylogenies (Kelleher et al, 2019). To this end we aimed to develop a general haplotype network model that could exploit phylogenetic relationships between haplotypes in a computationally efficient way.…”

Section: The Importance Of the Haplotype Network Modelmentioning

confidence: 99%

“…We here approach the problem of estimating haplotype effects by leveraging phylogenetic relationships between haplotypes described with a directed acyclic graph (DAG) and developing a hierarchical model of haplotype effects on this graph. We were inspired by recent advances in building phylogenies on large datasets (Kelleher et al, 2019) and aimed to develop a hierarchical model that could scale to a large number of haplotypes. Our work extends the phylogenetic mixed modeling of the whole genome (Lynch, 1991;Pagel, 1999;Housworth et al, 2004;Hadfield and Nakagawa, 2010) to a specific region.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical modeling of haplotype effects based on a phylogeny

Selle

Steinsland

Lindgren³

et al. 2020

Preprint

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This paper introduces a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are usually many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially -few haplotypes have high frequency and many haplotypes have low frequency. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations and leveraging these similarities can improve the estimation of haplotype effects. There is extensive literature on this topic. Here we build on these observations and develop an autoregressive model of order one that hierarchically models haplotype effects by leveraging phylogenetic relationships between the haplotypes described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network and we therefore refer to the model as the haplotype network model. The haplotype network model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. The key contribution of this work is that by leveraging the haplotype network structure we obtain a sparse model and by using hierarchical autoregression the flow of information between similar haplotypes is estimated from the data. We show with a simulation study that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially when there are few observations for a specific haplotype.We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a case study of modeling the effect of mitochondrial haplotypes on milk yield in cattle.

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Inference of natural selection from ancient DNA

Dehasque

Ávila‐Arcos²,

Díez-del-Molino

et al. 2020

Evolution Letters

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Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes.Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this 9 4Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists.In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods. K E Y W O R D S : Adaptation, ancient DNA, natural selection, paleogenomics, time series. Impact SummaryThe search for signatures of natural selection on the genome is still most commonly based on screening modern genomes for regions of reduced diversity or increased differentiation between populations. This framework is essentially a snapshot in time of a process that may have played out over many millennia, during which changes in population size, ecology and gene flow between populations may have played a role in determining genetic variation. Here, we outline how utilising ancient DNA (aDNA) techniques to sequence time series of genomes spanning changes in natural selection can provide a more nuanced understanding of how natural selection has impacted genomic variation in present-day populations. In particular, we argue that the advent of paleo-population genomics, in which datasets of multiple individuals spanning millennia have been sequenced, offers unprecedented opportunity to estimate changes in allele frequencies through time. We outline considerations and the types of data that would be needed for the inference of positive selection on traits associated with single and many genes (polygenic), genome-wide negative (background) selection, and balancing selection. However, we recognise that there are currently few datasets existing that are suitable for these types of investigation. There is thus a bias towards study species that have undergone strong selection over relatively recent timescales that are within the scope of aDNA, such as has occurred in domesticated species. We also detail a number of caveats associated with working with aDNA data, which is by its nature comprised of short, degraded DNA fragments, typicall...

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Inferring whole-genome histories in large population datasets

Cited by 233 publications

References 64 publications

Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

Hierarchical modeling of haplotype effects based on a phylogeny

Inference of natural selection from ancient DNA

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