Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data ...dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725-0.728 for severe, 0.631-0.635 for moderate, 0.598-0.602 for slight, and 0.708-0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690-0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.
A Model of Colonic Crypts using SBML Spatial Ramazzotti, Daniele; Maj, Carlo; Antoniotti, Marco
Electronic proceedings in theoretical computer science,
01/2013, Letnik:
130, Številka:
Proc. Wivace 2013
Journal Article
Odprti dostop
The Spatial Processes package enables an explicit definition of a spatial environment on top of the normal dynamic modeling SBML capabilities. The possibility of an explicit representation of spatial ...dynamics increases the representation power of SBML. In this work we used those new SBML features to define an extensive model of colonic crypts composed of the main cellular types (from stem cells to fully differentiated cells), alongside their spatial dynamics.
Abstract
Summary
The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with ...larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats.
Availability and implementation
GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk).
Supplementary information
Supplementary data are available at Bioinformatics online.
Epidemiological and clinical studies have provided evidence for a role of both genetic and environmental factors, such as stressful experiences early in life, in the pathogenesis of Schizophrenia ...(SZ) and microRNAs (miRNAs) have been suggested to play a key role in the interplay between the environment and our genome.
In this study, we conducted a miRNOme analysis in different samples (blood of adult subjects exposed to childhood trauma, brain (hippocampus) of rats exposed to prenatal stress and human hippocampal progenitor cells treated with cortisol) and we identified miR-125b-1-3p as a down-regulated miRNA in all the three datasets. Interestingly, a significant down-regulation was observed also in SZ patients exposed to childhood trauma. To investigate the biological systems targeted by miR-125b-1-3p and also involved in the effects of stress, we combined the list of biological pathways modulated by predicted and validated target genes of miR-125b-1-3p, with the biological systems significantly regulated by cortisol in the in vitro model. We found, as common pathways, the CXCR4 signaling, the G-alpha signaling, and the P2Y Purigenic Receptor Signaling Pathway, which are all involved in neurodevelopmental processes.
Our data, obtained from the combining of miRNAs datasets across different tissues and species, identified miR-125b-1-3p as a key marker associated with the long-term effects of stress early in life and also with the enhanced vulnerability of developing SZ. The identification of such a miRNA biomarker could allow the early detection of vulnerable subjects for SZ and could provide the basis for the development of preventive therapeutic strategies.
Reaction systems are a formal model based on the regulation mechanisms of facilitation and inhibition between biochemical reactions, which underlie the functioning of living cells. The aim of this ...paper is to explore the expressive power of reaction systems as a modeling framework, showing how their basic assumptions and properties can be exploited to formalize computer science and biology oriented problems. In this view, we first provide a reaction-based description of an iterative algorithm to solve the Tower of Hanoi puzzle. Then, we show how the regulation of gene expression in the lac operon, involved in the metabolism of lactose in Escherichia coli cells, can be formalized in terms of reaction systems. Finally, we present a method to derive, given a reaction system with n reactions, a functionally equivalent system with n′≤n reactions using simplification methods of boolean expressions. Some final remarks and directions for future research conclude the paper.
IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and ...biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. OBJECTIVES: To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models’ geographic generalizability; to test and integrate clinicians’ predictions; and to maximize clinical utility by building a sequential prognostic system. DESIGN, SETTING, AND PARTICIPANTS: This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. MAIN OUTCOMES AND MEASURES: Accuracy and generalizability of prognostic systems. RESULTS: A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean SD age, 25.1 5.8 years; 354 53.0% female and 314 47.0% male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. CONCLUSIONS AND RELEVANCE: These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms’ and clinicians’ risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser ...models for genome‐wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine‐mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome‐wide association studies, our three‐step approach enables to select and fit multivariable regression models on large‐scale genotype data. Based on UK Biobank data, we develop prediction models for LDL‐cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine‐mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models.
Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge ...in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the
is a commonly used measure to evaluate prediction accuracy. While the
is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results.
Based on large-scale genotype data from the UK Biobank, we compare three definitions of the
on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries.
Our analysis shows that the choice of the
definition can lead to considerably different results on test data, making the comparison of
values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the
based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of
can provide valuable complementary information.
Awareness of the different definitions of the
on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting
values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.
We aimed to investigate to what extent polygenic risk scores (PRS), rare pathogenic germline variants (PVs), and family history jointly influence breast cancer and prostate cancer risk.
A total of ...200,643 individuals from the UK Biobank were categorized as follows: (1) heterozygotes or nonheterozygotes for PVs in moderate to high-risk cancer genes, (2) PRS strata, and (3) with or without a family history of cancer. Multivariable logistic regression and Cox proportional hazards models were used to compute the odds ratio across groups and the cumulative incidence through life.
Cumulative incidence by age 70 years among the nonheterozygotes across PRS strata ranged from 9% to 32% and from 9% to 35% for breast cancer and prostate cancer, respectively. Among the PV heterozygotes it ranged from 20% to 48% in moderate-risk genes and from 51% to 74% in high-risk genes for breast cancer, and it ranged from 30% to 59% in prostate cancer risk genes. Family history was always associated with an increased cancer odds ratio.
PRS alone provides a meaningful risk gradient leading to a cancer risk stratification comparable to PVs in moderate risk genes, whereas acts as a risk modifier when considering high-risk genes. Including family history along with PV and PRS further improves cancer risk stratification.
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