Biology subjects

Fiorillo, E.

Publications and source records attributed to Fiorillo, E..

6 recordsLinked to original sources

bioRxiv2025

Quantifying Uncertainty in Polygenic Risk Scores Using Conformalized Quantile Regression

Polygenic risk scores (PRS) are widely used in post-GWAS analyses to predict complex traits across humans, animals, and plants, yet the uncertainty of these predictions is rarely quantified at the individual level. Here, we introduce a framework for individualized uncertainty quantification based on quantile regression and conformal prediction, enabling the construction of prediction intervals with guaranteed coverage under minimal assumptions. Quantile regression enables adaptive, individual-specific prediction intervals that capture asymmetry and allow interval widths to vary substantially across individuals based on genetic information alone. Applying this framework to 62 traits in the UK Biobank and the ProgeNIA/SardiNIA studies, we show that these intervals maintain valid coverage and reduce uncertainty in risk stratification compared to existing methods, driven by their adaptive construction. Prediction interval width correlates positively with age and BMI, indicating reduced genetic predictability in subsets of the population where genetic effects interact with environmental factors. Our results demonstrate that incorporating uncertainty is essential for interpreting polygenic predictions and provide a principled approach to distinguish individuals whose phenotypes are well explained by genetic predictors from those in whom non-genetic influences dominate.

bioinformatics↗

bioRxiv2025

DNMT3A-R882 mutations intrinsically drive dysfunctional neutropoiesis from human haematopoietic stem cells

Clonal haematopoiesis (CH) arises from the expansion of hematopoietic stem cells (HSCs) carrying leukaemia-associated somatic mutations. CH is linked to pathological immune dysregulation and a greater risk of age-related inflammatory diseases. Yet, how CH mutations impact HSC differentiation into immune effector cells remains understudied. Here, we report a single-cell resolution functional and multi-omic investigation of HSC clonal and differentiation dynamics in individuals with DNMT3A-R882 CH. DNMT3A-R882 reshapes the clonal architecture of haematopoiesis towards an aged phylogenetic structure. Functionally, DNMT3A-R882 HSCs produce decreased monocytic output but more abundant and mature neutrophil progeny compared to WT HSCs in the same individual. Whereas DNMT3A-R882 myeloid progenitors display attenuated inflammatory transcriptional programmes, DNMT3A- R882 mature neutrophils acquire proinflammatory and immunomodulatory features typical of maladaptive immunity and CH co-morbidities. Our findings, validated in humanised mice, identify aberrant DNMT3A-R882 HSC-driven neutropoiesis as a key link between CH, immune dysregulation and risk of inflammatory disease.

cell biology↗

bioRxiv2025

Regenie.QRS: computationally efficient whole-genome quantile regression at biobank scale

Genotype-phenotype associations can be context-dependent and dynamic in nature leading to heterogeneity of genetic effects across different parts of the phenotype distribution. Quantile regression, an alternative to linear regression for continuous phenotypes, is particularly well suited for detecting and characterizing heterogeneous genotype-phenotype associations. Here we propose a novel and computationally efficient whole-genome quantile regression technique, Regenie.QRS, for biobank-scale GWAS data with genetic structure. Our approach first estimates the polygenic effect, and then incorporates this effect as an offset in the non-mixed quantile regression model. Our simulations demonstrate robust control of type I error and higher power to detect heterogeneous associations relative to linear regression in GWAS, and improved power over the marginal quantile regression tests. We present applications using data from the UK Biobank and the ProgeNIA/SardiNIA project, where we show the advantages of Regenie.QRS in identifying and characterizing heterogeneous genetic effects. To cite just one interesting example, using quantile regression we are able to show that even though variants at the G6PC2 locus increase glucose levels, their effects are much stronger at lower quantiles of glucose level distribution than at higher quantiles, showing that G6PC2 serves as a guardian against low glucose levels without driving dangerous hyperglycemia, which may explain the lack of association with diabetes risk.

bioinformatics↗

bioRxiv2025

Quantile-specific confounding: correction for subtle population stratification via quantile regression

Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK biobank and the SardiNIA/ProgeNIA project for demonstration.

genetics↗

bioRxiv2025

Clinical progression of clonal hematopoiesis is determined by a combination of mutation timing, fitness, and clonal structure

Clonal hematopoiesis (CH) is characterized by expanding blood cell clones carrying somatic mutations in healthy aged individuals and is associated with various age-related diseases and all-cause mortality. While CH mutations affect diverse genes associated with myeloid malignancies, their mechanisms of expansion and disease associations remain poorly understood. We investigate the relationship between clonal fitness and clinical outcomes by integrating data from three longitudinal aging cohorts (n=713, observations=2,341). We demonstrate pathway-specific fitness advantage and clonal composition influence clonal dynamics. Further, the timing of mutation acquisition is necessary to determine the extent of clonal expansion reached during the host individuals lifetime. We introduce MACS120, a metric combining mutation context, timing, and variant fitness to predict future clonal growth, outperforming traditional variant allele frequency measurements in predicting clinical outcomes. Our unified analytical framework enables standardized clonal dynamics inference across cohorts, advancing our ability to predict and potentially intervene in CH-related pathologies.

genetics↗

bioRxiv2021

The longitudinal dynamics and natural history of clonal haematopoiesis

Human cells acquire somatic mutations throughout life, some of which can drive clonal expansion. Such expansions are frequent in the haematopoietic system of healthy individuals and have been termed clonal haematopoiesis (CH). While CH predisposes to myeloid neoplasia and other diseases, we have limited understanding of how and when CH develops, what factors govern its behaviour, how it interacts with ageing and how these variables relate to malignant progression. Here, we track 697 CH clones from 385 individuals aged 55 or older over a median of 13 years. We find that 92.4% of clones expanded at a stable exponential rate over the study period, with different mutations driving substantially different growth rates, ranging from 5% (DNMT3A, TP53) to over 50%/yr (SRSF2-P95H). Growth rates of clones with the same mutation differed by approximately +/-5%/yr, proportionately impacting "slow" drivers more substantially. By combining our time-series data with phylogenetic analysis of 1,731 whole genome-sequenced haematopoietic colonies from 7 older individuals, we reveal distinct patterns of lifelong clonal behaviour. DNMT3A-mutant clones preferentially expanded early in life and displayed slower growth in old age, in the context of an increasingly competitive oligoclonal landscape. By contrast, splicing gene mutations only drove expansion later in life, while growth of TET2-mutant clones showed minimal age-dependency. Finally, we show that mutations driving faster clonal growth carry a higher risk of malignant progression. Our findings characterise the lifelong natural history of CH and give fundamental insights into the interactions between somatic mutation, ageing and clonal selection.

cancer biology↗