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1. Recommendations for improving statistical inference in population genomics

   https://doi.org/10.1371/journal.pbio.3001669  

   Johri, Parul; Aquadro, Charles F.; Beaumont, Mark; Charlesworth, Brian; Excoffier, Laurent; Eyre-Walker, Adam; Keightley, Peter D.; Lynch, Michael; McVean, Gil; Payseur, Bret A.; et al (May 2022, PLOS Biology)

   The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties. 

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2. Detecting deviations from Kingman coalescence using 2-site frequency spectra

   https://doi.org/10.1093/genetics/iyaf023  

   Fenton, Eliot F; Rice, Daniel P; Novembre, John; Desai, Michael M (February 2025, GENETICS)

   Lohse, K (Ed.)

   Abstract Demographic inference methods in population genetics typically assume that the ancestry of a sample can be modeled by the Kingman coalescent. A defining feature of this stochastic process is that it generates genealogies that are binary trees: no more than 2 ancestral lineages may coalesce at the same time. However, this assumption breaks down under several scenarios. For example, pervasive natural selection and extreme variation in offspring number can both generate genealogies with “multiple-merger” events in which more than 2 lineages coalesce instantaneously. Therefore, detecting violations of the Kingman assumptions (e.g. due to multiple mergers) is important both for understanding which forces have shaped the diversity of a population and for avoiding fitting misspecified models to data. Current methods to detect deviations from Kingman coalescence in genomic data rely primarily on the site frequency spectrum (SFS). However, the signatures of some non-Kingman processes (e.g. multiple mergers) in the SFS are also consistent with a Kingman coalescent with a time-varying population size. Here, we present a new statistical test for determining whether the Kingman coalescent with any population size history is consistent with population data. Our approach is based on information contained in the 2-site joint frequency spectrum (2-SFS) for pairs of linked sites, which has a different dependence on the topologies of genealogies than the SFS. Our statistical test is global in the sense that it can detect when the genome-wide genetic diversity is inconsistent with the Kingman model, rather than detecting outlier regions, as in selection scan methods. We validate this test using simulations and then apply it to demonstrate that genomic diversity data from Drosophila melanogaster is inconsistent with the Kingman coalescent. 

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3. Distributed statistical inference with pyhf enabled through funcX

   https://doi.org/10.1051/epjconf/202125102070  

   Feickert, Matthew; Heinrich, Lukas; Stark, Giordon; Galewsky, Ben (January 2021, EPJ Web of Conferences)

   Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A. (Ed.)

   In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis of data from the Large Hadron Collider, but can pose problems with orchestration and efficient scheduling. The compute architectures at these facilities do not easily support the Python compute model, and the configuration scheduling of batch jobs for physics often requires expertise in multiple job scheduling services. The combination of the pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses of performing statistical inference with binned models, that would traditionally take multiple hours and bespoke scheduling, to an on-demand (fitting) “function as a service” that can scalably execute across workers in just a few minutes, offering reduced time to insight and inference. We demonstrate execution of a scalable workflow using funcX to simultaneously fit 125 signal hypotheses from a published ATLAS search for new physics using pyhf with a wall time of under 3 minutes. We additionally show performance comparisons for other physics analyses with openly published probability models and argue for a blueprint of fitting as a service systems at HPC centers. 

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4. A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types

   https://doi.org/10.3389/fgene.2020.587887  

   Zhu, Huanhuan; Shang, Lulu; Zhou, Xiang (January 2021, Frontiers in Genetics)

   Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types. 

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5. Accounting for Chimerism in Demographic Inference: Reconstructing the History of Common Marmosets ( Callithrix jacchus ) from High-Quality, Whole-Genome, Population-Level Data

   https://doi.org/10.1093/molbev/msaf119  

   Soni, Vivak; Versoza, Cyril J; Vallender, Eric J; Jensen, Jeffrey D; Pfeifer, Susanne P (June 2025, Molecular Biology and Evolution)

   Harris, Kelley (Ed.)

   Abstract As a species of considerable biomedical importance, characterizing the evolutionary genomics of the common marmoset (Callithrix jacchus) is of significance across multiple fields of research. However, at least 2 peculiarities of this species potentially preclude commonly utilized population genetic modeling and inference approaches: a high frequency of twin births and hematopoietic chimerism. We here investigate these effects within the context of demographic inference, demonstrating via simulation that neglecting these biological features results in significant mis-inference of the underlying population history. Based upon this result, we develop a novel approximate Bayesian inference approach accounting for both common twin births and chimeric sampling. In addition, we newly present population genomic data from 15 individuals sequenced to high coverage and utilize gene-level annotations to identify neutrally evolving intergenic regions appropriate for demographic inference. Applying our developed methodology, we estimate a well-fitting population history for this species, which suggests robust ancestral and current population sizes, as well as a size reduction roughly 7,000 years ago likely associated with a shift from arboreal to savanna vegetation in north-eastern Brazil during this period. 

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