Methods (new papers)

[pmokeefe]

Thread for the titles and abstracts of new papers about DNA/genealogical methods.
Discussion thread here: https://anthrogenica.com/showthread....s-(discussion).

On the number of genealogical ancestors tracing to the source groups of an admixed population


Jazlyn A. Mooney, Lily Agranat-Tamir, Jonathan K. Pritchard, Noah A. Rosenberg
In genetically admixed populations, admixed individuals possess ancestry from multiple source groups. Studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral populations. However, the same numerical ancestry fraction can represent a wide array of admixture scenarios. Using a mechanistic model of admixture, we characterize admixture genealogically: how many distinct ancestors from the source populations does the admixture represent? We consider African Americans, for whom continent-level estimates produce a 75-85% value for African ancestry on average and 15-25% for European ancestry. Genetic studies together with key features of African-American demographic history suggest ranges for model parameters. Using the model, we infer that if genealogical lineages of a random African American born during 1960-1965 are traced back until they reach members of source populations, the expected number of genealogical lines terminating with African individuals is 314, and the expected number terminating in Europeans is 51. Across discrete generations, the peak number of African genealogical ancestors occurs for birth cohorts from the early 1700s. The probability exceeds 50% that at least one European ancestor was born more recently than 1835. Our genealogical perspective can contribute to further understanding the admixture processes that underlie admixed populations. For African Americans, the results provide insight both on how many of the ancestors of a typical African American might have been forcibly displaced in the Transatlantic Slave Trade and on how many separate European admixture events might exist in a typical African-American genealogy.

[pmokeefe]

Statistical analysis and simulation allowing simultaneously positive, negative, and no crossover interference in multilocus recombination data
Shaul Sapielkin, Zeev Frenkel, Eyal Privman, Abraham B. Korol
doi: https://doi.org/10.1101/2022.11.02.514815
This article is a preprint and has not been certified by peer review
Abstract
Crossover interference (COI) is a widespread feature of homologous meiotic recombination. It can be quantified by the classical coefficient of coincidence (CoC) `but this characteristic is highly variable and specific to the pair of chromosomal intervals considered. Several models were proposed to characterize COI on a chromosome-wise level. In the gamma model, the strength of interference is characterized by a shape parameter ν, while the gamma-sprinkled two-pathway model (GS) accounts for both interference-dependent and independent crossover (CO) events by fitting a mixture of gamma distributions with v>1 and v=1, correspondingly, and mixture proportions 1-p and p. In reality, COI can vary along chromosomes resulting in low compliance of the fitted model to real data. Additional inconsistency can be caused by common neglecting of possible negative COI in the model, earlier reported for several organisms. In this work, we propose an extension of the GS-model to take possible negative COI into account. We propose a way for data simulation and parameter estimation for such situations.

[pmokeefe]

Ultra-fast genome-wide inference of pairwise coalescence times
Regev Schweiger, Richard Durbin
doi: https://doi.org/10.1101/2023.01.06.522935
This article is a preprint and has not been certified by peer review

Abstract
The pairwise sequentially Markovian coalescent (PSMC) algorithm and its extensions infer the coalescence time of two homologous chromosomes at each genomic position. This inference is utilized in reconstructing demographic histories, detecting selection signatures, genome-wide association studies, constructing ancestral recombination graphs and more. Inference of coalescence times between each pair of haplotypes in a large dataset is of great interest, as they may provide rich information about the population structure and history of the sample. We introduce a new method, Gamma-SMC, which is >14 times faster than current methods. To obtain this speed up, we represent the posterior coalescence time distributions succinctly as a Gamma distribution with just two parameters; while in PSMC and its extensions, these are held as a vector over discrete intervals of time. Thus, Gamma-SMC has constant time complexity per site, without dependence on a number of discrete time states. Additionally, due to this continuous representation, our method is able to infer times spanning many orders of magnitude, and as such is robust to parameter misspecification. We describe how this approach works, illustrate its performance on simulated and real data, and use it to study recent positive selection in the 1000 Genomes Project dataset.

[pmokeefe]

KIN: a method to infer relatedness from low-coverage ancient DNA
Divyaratan Popli, Stéphane Peyrégne & Benjamin M. Peter*
Genome Biology volume*24, Article*number:*10 (2023)

Abstract
Genetic kinship of ancient individuals can provide insights into their culture and social hierarchy, and is relevant for downstream genetic analyses. However, estimating relatedness from ancient DNA is difficult due to low-coverage, ascertainment bias, or contamination from various sources. Here, we present KIN, a method to estimate the relatedness of a pair of individuals from the identical-by-descent segments they share. KIN accurately classifies up to 3rd-degree relatives using at least 0.05x sequence coverage and differentiates siblings from parent-child*pairs. It incorporates additional models to adjust for contamination and detect inbreeding, which improves classification accuracy.

[pmokeefe]

Modeling of African population history using f-statistics can be highly biased and is not addressed by previously suggested SNP ascertainment schemes

Pavel Flegontov, Ulaş Işıldak, Robert Maier, Eren Yüncü, Piya Changmai, David Reich
doi: https://doi.org/10.1101/2023.01.22.525077
This article is a preprint and has not been certified by peer review

Abstract
f-statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. These statistics can provide strong evidence for either admixture or cladality, which can be robust to substantial rates of errors or missing data. f-statistics are guaranteed to be unbiased under “SNP ascertainment” (analyzing non-randomly chosen subsets of single nucleotide polymorphisms) only if it relies on a population that is an outgroup for all groups analyzed. However, ascertainment on a true outgroup that is not co-analyzed with other populations is often impractical and uncommon in the literature. In this study focused on practical rather than theoretical aspects of SNP ascertainment, we show that many non-outgroup ascertainment schemes lead to false rejection of true demographic histories, as well as to failure to reject incorrect models. But the bias introduced by common ascertainments such as the 1240K panel is mostly limited to situations when more than one sub-Saharan African and/or archaic human groups (Neanderthals and Denisovans) or non-human outgroups are co-modelled, for example, f4-statistics involving one non-African group, two African groups, and one archaic group. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, cannot fix all these problems since for some classes of f-statistics it is not a clean outgroup ascertainment, and in other cases it demonstrates relatively low power to reject incorrect demographic models since it provides a relatively small number of variants common in anatomically modern humans. And due to the paucity of high-coverage archaic genomes, archaic individuals used for ascertainment often act as sole representatives of the respective groups in an analysis, and we show that this approach is highly problematic. By carrying out large numbers of simulations of diverse demographic histories, we find that bias in inferences based on f-statistics introduced by non-outgroup ascertainment can be minimized if the derived allele frequency spectrum in the population used for ascertainment approaches the spectrum that existed at the root of all groups being co-analyzed. Ascertaining on sites with variants common in a diverse group of African individuals provides a good approximation to such a set of SNPs, addressing the great majority of biases and also retaining high statistical power for studying population history. Such a “pan-African” ascertainment, although not completely problem-free, allows unbiased exploration of demographic models for the widest set of archaic and modern human populations, as compared to the other ascertainment schemes we explored.

[PLogan]

https://academic.oup.com/bioinformat...tad028/6986969

We introduce mapache, a flexible, robust and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large datasets—such as reference panels and multiple extracts and libraries per sample — to one or several genomes. Mapache can easily be customized or combined with other Snakemake tools.

[pmokeefe]

Bayesian inference of admixture graphs on Native American and Arctic populations | PLOS Genetics
https://journals.plos.org/plosgeneti...l.pgen.1010410
Svend V. Nielsen, Andrew H. Vaughn, Kalle Leppälä, Michael J. Landis, Thomas Mailund, Rasmus Nielsen x
Published: February 13, 2023
https://doi.org/10.1371/journal.pgen.1010410

This is an uncorrected proof.
Abstract
Admixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to characterize the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans.