Biology subjects

Wiuf, C.

Publications and source records attributed to Wiuf, C..

10 recordsLinked to original sources

bioRxiv2026

Calibration improves estimation of linkage disequilibrium on low sample sizes

Linkage disequilibrium is a central statistic in population genetic studies, commonly measured by the squared correlation between pairs of genetic variants. An important drawback of this measure is its upward bias caused by a finite sample size. To handle this, different methods exist that correct for sample-size bias. However, because the correlation consists of a ratio, there is no unbiased method to compute it. In this work, we present a procedure to calibrate those methods using a non-parametric approach with simulated data. This is done with forward modeling to generate genotype matrices with known parameters, followed by an inverse mapping to recover estimates of the underlying parameters. Then, a mean-centering calibration is applied to the recovered estimate of the true parameter. This approach is applied to real and simulated data, showing consistent improvement in accuracy compared to other sample-size-aware methods. Furthermore, to study the effects on downstream analyses, we analyze the classification performance on LD pruning, where we also observe an improvement, particularly in extreme cases with low sample sizes of 5 or 10 individuals.

bioinformatics↗

bioRxiv2024

Measuring linkage disequilibrium and improvement of pruning and clumping in structured populations

Standard measures of linkage disequilibrium (LD) are affected by admixture and population structure, such that loci that are not in LD within each ancestral population appear linked when considered jointly. The influence of population structure on LD can cause problems for downstream analysis methods, in particular those that rely on LD pruning or clumping. To address this issue, we propose a measure of LD that accommodates population structure using the top inferred principal components. We estimate LD from the correlation of geno-type residuals and prove that this LD measure remains unaffected by population structure when analyzing multiple populations jointly, even with admixed individuals. Based on this adjusted measure of LD, we can perform LD pruning to remove the correlation between markers for downstream analysis. Traditional LD pruning is more likely to remove markers with high differences in allele frequencies between populations, which biases measures for genetic differentiation and removes markers that are not in LD in the ancestral populations. Using data from moderately differentiated human populations and highly differentiated giraffe populations we show that traditional LD pruning biases FST and PCA but that this can be alleviated with the adjusted LD measure. In addition, we show the adjusted LD leads to better PCA when pruning and that LD clumping retains more sites and the retained sites have stronger associations.

genetics↗

bioRxiv2024

Inferring drift, genetic differentiation, and admixture graphs from low-depth sequencing data

A number of popular methods for inferring the evolutionary relationship between populations require essentially two components: First, they require estimates of f2-statistics, or some quantity that is a linear combination of these. Second, they require estimates of the variability of the statistic in question. Examples of methods in this class include qpGraph and TreeMix. It is known, however, that these statistics are biased when based on genotype calls at low depth. Moreover, as we show, this leads to downstream inference of significantly distorted trees. To solve this problem, we demonstrate how to accurately and efficiently compute a broad class of statistics from low-depth whole-genome sequencing data, including estimates of their standard errors, by using the site frequency spectrum. In particular, we focus on f2 and the sample covariance of allele frequencies to show how this method leads to accurate estimate of drift when fitting trees using qpGraph and TreeMix with low-depth data. However, the same considerations lead to uncertainty estimates for a variety of other statistics, including heterozygosity, kinship estimates (e.g. King), and quantities relating to genetic differentiation such as Fst and Dxy.

bioinformatics↗

bioRxiv2023

Evaluation of population structure inferred by principal component analysis or the admixture model

Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome wide human data we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.

genetics↗

bioRxiv2022

Estimating admixture pedigrees of recent hybrids without a contiguous reference genome

The genome of recently admixed individuals or hybrids have characteristic genetic patterns that can be used to learn about their recent admixture history. One of these are patterns of interancestry heterozygosity, which can be inferred from SNP data from either called genotypes or genotype likelihoods, without the need for information on genomic location. This makes them applicable to a wide range of data that are often used in evolutionary and conservation genomic studies, such as low-depth sequencing mapped to scaffolds and reduced representation sequencing. Here we implement maximum likelihood estimation of interancestry heterozygosity patterns using two complementary models. We furthermore develop apoh (Admixture Pedigrees Of Hybrids), a software that uses estimates of paired ancestry proportions to detect recently admixed individuals or hybrids, and to find the most compatible recent admixture pedigree. It furthermore calculates several hybrid indices that make it easier to identify and rank possible admixture pedigrees that could give rise to the estimated patterns. We implemented apoh both as a command line tool and as a Graphical User Interface that allows the user to automatically and interactively explore, rank and visualize compatible recent admixture pedigrees, and calculate the different summary indices. We validate the performance of the method using admixed family trios from the 1000 Genomes Project. In addition, we show its applicability on identifying recent hybrids from RAD-seq data of Grants gazelle (Nanger granti and Nanger petersii) and whole genome low depth data of waterbuck (Kobus ellipsiprymnus) which shows complex admixture of up to four populations.

bioinformatics↗

bioRxiv2022

The Behaviour of F-statistics over Time

We study the behaviour of the F2-statistic and Fst-statistic, respectively, over time in a Wright-Fisher model with mutation and migration. We give precise conditions for when the F2-statistic is non-monotonic, that is, increases over time until a certain point and then starts decreasing. We show that even for small population sizes, the two statistics are well approximated by population size scaled expressions.

genetics↗

bioRxiv2022

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage

The site frequency spectrum (SFS) is an important summary statistic in population genetics used for inference on demographic history and selection. However, estimation of the SFS from called genotypes introduce bias when working with low-coverage sequencing data. Methods exist for addressing this issue, but sometimes suffer from two problems. First, they can have very high computational demands, to the point that it may not be possible to run estimation for genome-scale data. Second, existing methods are prone to overfitting, especially for multi-dimensional SFS estimation. In this article, we present a stochastic expectation-maximisation algorithm for inferring the SFS from NGS data that addresses these challenges. We show that this algorithm greatly reduces runtime and enables estimation with constant, trivial RAM usage. Further, the algorithm reduces overfitting and thereby improves downstream inference. An implementation is available at github.com/malthesr/winsfs.

bioinformatics↗

bioRxiv2022

Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data

Parchment was used as a writing material in the Middle Ages and was made using animal skins by liming them with Ca(OH)2. During liming, collagen peptides containing Glutamine (Q) undergo deamidation resulting in a mass shift of 0.984 Da. Assessing the extent of deamidation can inform us about parchment production patterns and quality. In this study, we propose a simple three-step workflow, developed as an R package called MALDIpqi(), to estimate deamidation in parchment derived collagen using low-resolution MALDI-TOF spectra. After pre-processing raw spectra, we used weighted least-squares linear regression to estimate Q deamidation levels from the convoluted isotopic envelope for seven collagen-peptide markers. Finally, we employed a linear mixed effect model to predict the overall deamidation level of a parchment sample termed Parchment Glutamine Index (PQI). To test the robustness of the workflow, we applied MALDIpqi() to previously published ZooMS data generated from almost an entire library of the Cistercian monastery at Orval Abbey, Belgium. In addition to reliably predicting PQI, we observed interesting patterns pertaining to parchment production. MALDIpqi() holds excellent potential for biocodicological and other archaeological studies involving collagen, such as bone, but we also foresee its application in the food and biomedical industry.

bioinformatics↗

bioRxiv2019

Identification of hidden population structure in time-scaled phylogenies

Population structure influences genealogical patterns, however data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealised genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past, and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance.

evolutionary biology↗

bioRxiv2017

Improved D-Statistic For Low-Coverage Data

The detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups.\n\nWhen working with high throughput sequencing data calling genotypes accurately is not always possible, therefore the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes.\n\nWe provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate.\n\nWe prove that the D-statistic is approximated by a standard normal. Furthermore we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low/medium sequencing depth (1-10X) and performances are as good as with perfectly called genotypes at a sequencing depth of 2X. We show the reliability of error correction on scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.

bioinformatics↗