Efficacy and safety of shunt surgery in patients with idiopathic normal-pressure hydrocephalus: can we predict shunt response by preoperative magnetic resonance imaging (MRI)?

AIM: The aim of this study was to identify preoperative magnetic resonance imaging (MRI) findings that can predict the shunt responsiveness in idiopathic normal-pressure hydrocephalus (iNPH) patients and to investigate postoperative outcome and complications. MATERIALS AND METHODS: A total of 192 patients with iNPH who underwent shunt at our hospital between 2000 and 2021 were included to investigate complications. Of these, after exclusion, 127 (1-month postoperative follow-up) and 77 (1-year postoperative follow-up) patients were evaluated. The preoperative MRI features (the presence of tightness of the high-convexity subarachnoid space, Sylvian fissure enlargement, Evans' index, and callosal angle) of the shunt-response and nonresponse groups were compared, and a systematic review was conducted to evaluate whether preoperative MRI findings could predict shunt response. RESULTS: Postoperative complications within one month after surgery were observed in 6.8% (13/192), and the most common complication was hemorrhage. Changes in corpus callosum were observed in 4.2% (8/192). The shunt-response rates were 83.5% (106/127) in the 1-month follow-up group and 70.1% (54/77) in 1-year follow-up group. In the logistic regression analysis, only Evans' index measuring >0.4 had a significant negative relationship with shunt response at 1-month follow-up; however, no significant relationship was observed at 1-year follow-up. According to our systematic review, it is still controversial whether preoperative MRI findings could predict shunt response. CONCLUSION: Evans' index measure of >0.4 had a significant relationship with the shunt response in the 1-month follow-up group. In systematic reviews, there is ongoing debate about whether preoperative MRI findings can accurately predict responses to shunt surgery. Postoperative corpus callosal change was observed in 4.2% of iNPH patients.

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software.

BACKGROUND: For liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated. METHODS: Three physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve. RESULTS: The mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via. CONCLUSIONS: Liver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Clinical suspicion, diagnosis and management of cardiac amyloidosis: update document and executive summary.

In recent years, the interest in cardiac amyloidosis has grown exponentially. However, there is a need to improve our understanding of amyloidosis in order to optimise early detection systems. Therefore, it is crucial to incorporate solutions to improve the suspicion, diagnosis and follow-up of cardiac amyloidosis. In this sense, we designed a tool following the different phases to reach the diagnosis of cardiac amyloidosis, as well as an optimal follow-up: a) clinical suspicion, where the importance of the "red flags" to suspect it and activate the diagnostic process is highlighted; 2) diagnosis, where the diagnostic algorithm is mainly outlined; and 3) follow-up of confirmed patients. This is a practical resource that will be of great use to all professionals caring for patients with suspected or confirmed cardiac amyloidosis, to improve its early detection, as well as to optimise its accurate diagnosis and optimal follow-up.

The longitudinal behavioral effects of acute exposure to galactic cosmic radiation in female C57BL/6J mice: implications for deep space missions, female crews, and potential antioxidant countermeasures.

Galactic cosmic radiation (GCR) is an unavoidable risk to astronauts that may affect mission success. Male rodents exposed to 33-beam-GCR (33-GCR) show short-term cognitive deficits but reports on female rodents and long-term assessment is lacking. Here we asked: What are the longitudinal behavioral effects of 33-GCR on female mice? Also, can an antioxidant/anti-inflammatory compound mitigate the impact of 33-GCR? Mature (6-month-old) C57BL/6J female mice received the antioxidant CDDO-EA (400 µg/g of food) or a control diet (vehicle, Veh) for 5 days and either Sham-irradiation (IRR) or whole-body 33-GCR (0.75Gy) on the 4th day. Three-months post-IRR, mice underwent two touchscreen-platform tests: 1) location discrimination reversal (which tests behavior pattern separation and cognitive flexibility, two abilities reliant on the dentate gyrus) and 2) stimulus-response learning/extinction. Mice then underwent arena-based behavior tests (e.g. open field, 3-chamber social interaction). At the experiment end (14.25-month post-IRR), neurogenesis was assessed (doublecortin-immunoreactive [DCX+] dentate gyrus neurons). Female mice exposed to Veh/Sham vs. Veh/33-GCR had similar pattern separation (% correct to 1st reversal). There were two effects of diet: CDDO-EA/Sham and CDDO-EA/33-GCR mice had better pattern separation vs. their respective control groups (Veh/Sham, Veh/33-GCR), and CDDO-EA/33-GCR mice had better cognitive flexibility (reversal number) vs. Veh/33-GCR mice. Notably, one radiation effect/CDDO-EA countereffect also emerged: Veh/33-GCR mice had worse stimulus-response learning (days to completion) vs. all other groups, including CDDO-EA/33-GCR mice. In general, all mice show normal anxiety-like behavior, exploration, and habituation to novel environments. There was also a change in neurogenesis: Veh/33-GCR mice had fewer DCX+ dentate gyrus immature neurons vs. Veh/Sham mice. Our study implies space radiation is a risk to a female crew's longitudinal mission-relevant cognitive processes and CDDO-EA is a potential dietary countermeasure for space-radiation CNS risks.

Exact and efficient phylodynamic simulation from arbitrarily large populations.

Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling (BDMS) model, there exists an equivalent pure birth process (i.e., no death) with mutation and complete sampling. The final trees generated under these processes have exactly the same distribution. Leveraging this property, we propose a highly efficient algorithm for simulating trees under a general BDMS model. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.

Improving the accuracy of nitrogen estimates from nonpoint source in a river catchment with multi-isotope tracers.

Climate change can affect precipitation patterns, temperature, and the hydrological cycle, consequently influencing the dynamics of nitrogen (N) within aquatic ecosystems. In this study, multiple stable isotopes (15N-NO3/18O-NO3 and 2H-H2O/18O-H2O) were used to investigate the N sources and flowpath within the Bogang stream in South Korea. Within the vicinity of the stream with complex land use where various N sources were present, four end-members (rainfall, soil, sewage, and livestock) were sampled and examined. Consequently, spatial-temporal variations of the N sources were observed dependent on the type of land use. During the dry season, sewage accounted for the dominant N source, ranging from 62.2 % to 80.2 %. In contrast, nonpoint sources increased significantly across most sites during the wet season (10.3-41.6 % for soil; 6.3-35.2 % for livestock) compared to the dry season (7.7-28.5 % for soil; 6-13.2 % for livestock). However, sewage (78.7 %) remains dominant, representing the largest ratio at the site downstream of the wastewater treatment plant during the wet season. This ratio showed a notable difference from the calculated N loading ratio of 52.2 %, especially for livestock. This suggests that a significant potential for N legacy effects, given that groundwater flow is likely to be the primary hydrological pathway delivering N to rivers. This study will help to develop water resource management strategies by understanding how the interaction between N sources and hydrological process responds to climate change within sub-basins.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects.

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction.

Whereas protein language models have demonstrated remarkable efficacy in predicting the effects of missense variants, DNA counterparts have not yet achieved a similar competitive edge for genome-wide variant effect predictions, especially in complex genomes such as that of humans. To address this challenge, we here introduce GPN-MSA, a novel framework for DNA language models that leverages whole-genome sequence alignments across multiple species and takes only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC, OMIM), experimental functional assays (DMS, DepMap), and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and non-coding variants.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data.

CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.

Tracing cancer evolution and heterogeneity using Hi-C.

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.

Predicting the effect of CRISPR-Cas9-based epigenome editing.

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we here use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ~ 0.70 - 0.79 for most samples. In addition to recapitulating known associations between histone PTMs and expression patterns, our models predict that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how engineered vs. natural deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold changes among genes in response to the dCas9-p300 system; however, their ability to rank fold changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.

DNA language models are powerful predictors of genome-wide variant effects.

The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN), a model designed to learn genome-wide variant effects through unsupervised pretraining on genomic DNA sequences. Our model also successfully learns gene structure and DNA motifs without any supervision. To demonstrate its utility, we train GPN on unaligned reference genomes of Arabidopsis thaliana and seven related species within the Brassicales order and evaluate its ability to predict the functional impact of genetic variants in A. thaliana by utilizing allele frequencies from the 1001 Genomes Project and a comprehensive database of GWAS. Notably, GPN outperforms predictors based on popular conservation scores such as phyloP and phastCons. Our predictions for A. thaliana can be visualized as sequence logos in the UCSC Genome Browser (https://genome.ucsc.edu/s/gbenegas/gpn-arabidopsis). We provide code (https://github.com/songlab-cal/gpn) to train GPN for any given species using its DNA sequence alone, enabling unsupervised prediction of variant effects across the entire genome.

Cross-protein transfer learning substantially improves disease variant prediction.

BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. RESULTS: We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. CONCLUSIONS: Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

CELL-E: A Text-To-Image Transformer for Protein Localization Prediction.

Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer model that generates 2D probability density images describing the spatial distribution of proteins within cells. Given an amino acid sequence and a reference image for cell or nucleus morphology, CELL-E predicts a more refined representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.

CherryML: scalable maximum likelihood estimation of phylogenetic models.

Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryML's utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.

Image findings of anti-neutrophil cytoplasmic antibody-associated vasculitis involving the skull base.

AIM: To investigate computed tomography (CT) and magnetic resonance imaging (MRI) features of skull bases involving anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV). MATERIALS AND METHODS: A retrospective review was undertaken to identify an institutional historical cohort of 17 patients with confirmed AAV who underwent CT or MRI and had skull base involvement between 2002 and 2021. Two radiologists reviewed the extent and features of the lesions, bone changes, and other MRI findings. RESULTS: A total of 17 patients (12 men; mean age ± standard deviation, 46.5 ± 17.1 years) were selected. AAV presented as infiltrative lesions with involvement at various sites. Most cases involved the paranasal sinuses (PNS; 88%, 15/17), nasopharynx (88%, 15/17), pterygopalatine fossa (82%, 14/17), and parapharyngeal space (82%, 14/17), frequently accompanied by mucosal irregularity of the PNS and nasopharynx (71%, 12/17). Central skull base and temporal bone involvement were seen in 53% (9/17) and 38% (6/16) of cases, respectively. On T1-weighted imaging (WI) and T2WI MRI, all lesions (15/15) showed predominant signal iso-intensity to grey matter. CONCLUSIONS: Although radiological findings of AAV are non-specific and skull base involvement is less common, AAV may be considered if infiltrative lesions predominantly involving the PNS, nasopharynx, pterygopalatine fossa, and parapharyngeal space with combined bone changes of skull base are seen.

Comparative analysis of vaginal microbiota sampling using menstrual cups and high vaginal swabs in pregnant women living with HIV-1 infection.

Background: Menstrual cups (MCs) are increasingly used to collect cervicovaginal secretions to characterise vaginal mucosal immunology, in conjunction with high vaginal swabs (HVS) for metataxonomics, particularly in HIV transmission studies. We hypothesised that both methods of collecting bacterial biomass are equivalent for 16S rRNA gene sequencing. Material and Methods: Cervicovaginal fluid (CVF) samples from 16 pregnant women with HIV-1 (PWWH) were included to represent the major vaginal bacterial community state types (CST I-V). Women underwent sampling during the second trimester by liquid amies HVS followed by a MC (Soft disc™) and samples were stored at -80°C. Bacterial cell pellets obtained from swab elution and MC (500 µL, 1 in 10 dilution) were resuspended in 120 µL PBS for DNA extraction. Bacterial 16S rRNA gene sequencing was performed using V1-V2 primers and were analysed using MOTHUR. Paired total DNA, bacterial load, amplicon read counts, diversity matrices and bacterial taxa were compared by sampling method using MicrobiomeAnalyst, SPSS and R. Results: The total DNA eluted from one aliquot of diluted CVF from an MC was similar to that of a HVS (993ng and 609ng, p=0.18); the mean bacterial loads were also comparable for both methods (MC: 8.0 log10 16S rRNA gene copies versus HVS: 7.9 log10 16S rRNA gene copies, p=0.27). The mean number of sequence reads generated from MC samples was lower than from HVS (MC: 12730; HVS:14830, p=0.05). The α-diversity metrices were similar for both techniques; MC Species Observed: 41 (range 12-96) versus HVS: 47 (range 16-96), p=0.15; MC Inverse Simpson Index: 1.98 (range 1.0-4.0) versus HVS: 0.48 (range 1.0-4.4), p=0.22). The three most abundant species observed were: Lactobacillus iners, Lactobacillus crispatus and Gardnerella vaginalis. Hierarchical clustering of relative abundance data showed that samples obtained using different techniques in an individual clustered in the same CST group. Conclusion: These data demonstrate that despite sampling slightly different areas of the lower genital tract, there was no difference in bacterial load or composition between methods. Both are suitable for characterisation of vaginal microbiota in PWWH. The MC offers advantages, including a higher volume of sample available for DNA extraction and complimentary assays.

A fast machine-learning-guided primer design pipeline for selective whole genome amplification.

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

The Impact of Stability Considerations on Genetic Fine-Mapping.

Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite its widespread use, however, residualization may not completely remove all sources of confounding. Here, we propose a complementary stability-guided approach that does not rely on residualization, which identifies consistently fine-mapped variants across different genetic backgrounds or environments. We demonstrate the utility of this approach by applying it to fine-map eQTLs in the GEUVADIS data. Using 378 different functional annotations of the human genome, including recent deep learning-based annotations (e.g., Enformer), we compare enrichments of these annotations among variants for which the stability and traditional residualization-based fine-mapping approaches agree against those for which they disagree, and find that the stability approach enhances the power of traditional fine-mapping methods in identifying variants with functional impact. Finally, in cases where the two approaches report distinct variants, our approach identifies variants comparably enriched for functional annotations. Our findings suggest that the stability principle, as a conceptually simple device, complements existing approaches to fine-mapping, reinforcing recent advocacy of evaluating cross-population and cross-environment portability of biological findings. To support visualization and interpretation of our results, we provide a Shiny app, available at: https://alan-aw.shinyapps.io/stability_v0/.