Biology subjects

Flagel, L.

Publications and source records attributed to Flagel, L..

4 recordsLinked to original sources

bioRxiv2023

The effects of reference panel perturbations on the accuracy of genotype imputation

Reference-based genotype imputation is a standard technique that has become increasingly popular in large-scale studies involving genomic data. The two key elements involved in the process of genotype imputation are (1) the haplotype reference panel to which a target individual is being imputed, and (2) the imputation algorithm used to infer missing genotypes in the target individual. The imputation literature has historically focused mainly on (2), with a typical comparative study investigating the relative performance of various imputation algorithms while holding the reference panel constant. However, the role of the reference panel itself (1) on overall imputation performance is equally, if not more, important than the choice among many high-performing algorithms. Even though it is intuitive that the quality of a reference panel should play a role in the accuracy of imputation, it is nonetheless unclear to what extent common errors during panel creation (e.g., genotyping and phase error) lead to suboptimal imputation performance. In this study, we investigate the effects of applying three distinct modes of perturbations to a widely used haplotype reference panel in human genetics on the resulting imputation accuracy. Specifically, we perturb the reference panel by (1) randomly introducing phase errors, (2) randomly introducing genotype errors, and (3) randomly pruning variants from the panel (all at varying magnitudes). We then impute a set of diverse individuals at various sequencing coverages (0.5x, 1.0x, and 2.0x) to these various perturbed panels and evaluate imputation accuracy using the r2 metric for the entire cohort as well as ancestry-stratified subsets. We observe that both phase- and genotype-perturbations can dramatically affect imputation accuracy, particularly at very low allele frequencies, while pruning variants has a far smaller effect. We then empirically verified that our simulations reliably predict the impact of potential filtering techniques in a real-world dataset. In the context of haplotype reference panels, these results suggest that phasing and genotyping accuracy are far more important than the density of a reference panel used for imputation.

genomics↗

bioRxiv2023

IntroUNET: identifying introgressed alleles via semantic segmentation

1A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient--ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individuals alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our methods success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data. 2 Author SummaryIt is now known that a sizeable fraction of species occasionally hybridize with related species. Thus, many species harbor genetic material that traces its ancestry to closely related species. For example, many humans contain DNA that was "introgressed" from Neanderthals. The growing appreciation of the commonality of introgression has sparked a keen interest in determining which portions of the genome were introgressed. Several statistical approaches have been devised for identifying the population genetic signatures of introgression, but the most powerful techniques for this task take advantage of modern machine learning techniques. Here, we describe a deep learning method for identifying segments of introgressed DNA. This method is based on neural networks used to determine which pixels in an image belong to which type of object. By treating a matrix of genotypes from a sample of individuals from two closely related species, we can use this deep learning approach to accurately infer which portions of which genomes from the first population were introgressed from the second, and vice-versa. We show that our method, which we have released as an open-source software package, is highly accurate using a variety of simulated scenarios and a real test case from the genus Drosophila.

evolutionary biology↗

bioRxiv2018

The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

Population-scale genomic datasets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information from genomic sequences to infer demographic events such as population size changes and gene flow among closely related populations/species, construct recombination maps, and uncover loci underlying recent adaptation. To date most methods make use of only one or a few summaries of the input sequences and therefore ignore potentially useful information encoded in the data. The most sophisticated of these approaches involve likelihood calculations, which require theoretical advances for each new problem, and often focus on a single aspect of the data (e.g. only allele frequency information) in the interest of mathematical and computational tractability. Directly interrogating the entirety of the input sequence data in a likelihood-free manner would thus offer a fruitful alternative. Here we accomplish this by representing DNA sequence alignments as images and using a class of deep learning methods called convolutional neural networks (CNNs) to make population genetic inferences from these images. We apply CNNs to a number of evolutionary questions and find that they frequently match or exceed the accuracy of current methods. Importantly, we show that CNNs perform accurate evolutionary model selection and parameter estimation, even on problems that have not received detailed theoretical treatments. Thus, when applied to population genetic alignments, CNN are capable of outperforming expert-derived statistical methods, and offer a new path forward in cases where no likelihood approach exists.

evolutionary biology↗

bioRxiv2018

A synthesis of mapping experiments reveals extensive genomic structural diversity in the Mimulus guttatus species complex

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. In this paper, we tackle this challenge by developing a novel statistical approach to comparative genetic mapping. The procedure couples a Hidden Markov Model with a Genetic Algorithm to detect large-scale structural variation using low-level sequencing data from multiple genetic mapping populations. We demonstrate the method using five distinct crosses within the flowering plant genus Mimulus. The synthesis of data from these experiments is first used to correct numerous errors (misplaced sequences) in the M. guttatus reference genome. Second, we confirm and/or detect eight large inversions polymorphic within the M. guttatus species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.\n\nAUTHOR SUMMARYGenome sequences have proved to be a critical experimental resource for genetic research in many species. However, in some species there is considerable variation in genomic organization, making a single reference genome sequence inadequate. This variation can cause issues in interpreting genomic signals, such as those coming from trait mapping. We introduce a new statistical method and computational tools that use linkage information to reorganize a single reference genome to 1) repair genome assembly errors, and 2) identify variation between individuals or populations of the same species. Using this method we can create a new genome order that improves upon the reference genome. We apply this method to five crosses among plants in the Mimulus guttatus species complex. In this system we detect eight large chromosomal inversions and improve the resolution of a trait mapping study. This work highlights the utility of our method, and indicates how others studying diverse species might use them to improve their own research.

genetics↗