New methods for inference of local tree topologies with recombinant SNP sequences in populations

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. However, this approach is out of step with some modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalizes these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach

Motivation

Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data.

Results

In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests.

Availability and implementation

The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix.

Supplementary information

Supplementary data are available at Bioinformatics online.

From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection

Methods to detect signals of natural selection from genomic data have traditionally emphasized the use of simple summary statistics. Here, we review a new generation of methods that consider combinations of conventional summary statistics and/or richer features derived from inferred gene trees and ancestral recombination graphs (ARGs). We also review recent advances in methods for population genetic simulation and ARG reconstruction. Finally, we describe opportunities for future work on a variety of related topics, including the genetics of speciation, estimation of selection coefficients, and inference of selection on polygenic traits. Together, these emerging methods offer promising new directions in the study of natural selection.

A method for genome-wide genealogy estimation for thousands of samples

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination

Motivation

: Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination.

Results

: In this paper, we present a new approach called RENT+  for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+  builds on a previous genealogy inference approach called RENT , which infers a set of related genealogical trees at different genomic positions. RENT+  represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT . The key components of RENT+  are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+  is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+  in the inference of population demographic history from haplotypes, which outperforms several existing methods.

Availability and Implementation

: RENT+  is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus .

Contacts

: sajad@engr.uconn.edu or ywu@engr.uconn.edu.

Supplementary information

: Supplementary data are available at Bioinformatics online.

Genome-wide inference of ancestral recombination graphs

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of [Formula: see text] chromosomes conditional on an ARG of [Formula: see text] chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.

Bayesian inference of local trees along chromosomes by the sequential Markov coalescent

We propose a genealogy-sampling algorithm, Sequential Markov Ancestral Recombination Tree (SMARTree), that provides an approach to estimation from SNP haplotype data of the patterns of coancestry across a genome segment among a set of homologous chromosomes. To enable analysis across longer segments of genome, the sequence of coalescent trees is modeled via the modified sequential Markov coalescent (Marjoram and Wall, Genetics 7:16, 2006). To assess performance in estimating these local trees, our SMARTree implementation is tested on simulated data. Our base data set is of the SNPs in 10 DNA sequences over 50 kb. We examine the effects of longer sequences and of more sequences, and of a recombination and/or mutational hotspot. The model underlying SMARTree is an approximation to the full recombinant-coalescent distribution. However, in a small trial on simulated data, recovery of local trees was similar to that of LAMARC (Kuhner et al. Genetics 156:1393-1401, 2000a), a sampler which uses the full model.

Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies

Background

In mammalian genetics, many quantitative traits, such as blood pressure, are thought to be influenced by specific genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone. In particular, the application of classical statistical methods to single nucleotide polymorphism (SNP) data collected in genome-wide association studies has been especially challenging. We propose a coalescent approach to search for SNPs associated with quantitative traits in genome-wide association study (GWAS) data by taking into account the evolutionary history among SNPs.

Results

We evaluate the performance of the new method using simulated data, and find that it performs at least as well as existing methods with an increase in performance in the case of population structure. Application of the methodology to a real data set consisting of high-density lipoprotein cholesterol measurements in mice shows the method performs well for empirical data, as well.

Conclusions

By combining methods from stochastic processes and phylogenetics, this work provides an innovative avenue for the development of new statistical methodology in the analysis of GWAS data.