Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Clusters of individuals recovering from an exacerbation of chronic obstructive pulmonary disease and response to in-hospital pulmonary rehabilitation

Introduction and objectives: Due to the present low availability of pulmonary rehabilitation (PR) for individuals recovering from a COPD exacerbation (ECOPD), we need admission priority criteria. We tested the hypothesis that these individuals might be clustered according to baseline characteristics to identify subpopulations with different responses to PR. Methods: Multicentric retrospective analysis of individuals undergone in-hospital PR. Baseline characteristics and outcome measures (six-minute walking test - 6MWT, Medical Research Council scale for dyspnoea -MRC, COPD assessment test -CAT) were used for clustering analysis. Results: Data analysis of 1159 individuals showed that after program, the proportion of individuals reaching the minimal clinically important difference (MCID) was 85.0%, 86.3%, and 65.6% for CAT, MRC, and 6MWT respectively. Three clusters were found (C1-severe: 10.9%; C2-intermediate: 74.4%; C3-mild: 14.7% of cases respectively). Cluster C1-severe showed the worst conditions with the largest post PR improvements in outcome measures; C3-mild showed the least severe baseline conditions, but the smallest improvements. The proportion of participants reaching the MCID in ALL three outcome measures was significantly different among clusters, with C1-severe having the highest proportion of full success (69.0%) as compared to C2-intermediate (48.3%) and C3-mild (37.4%). Participants in C2-intermediate and C1-severe had 1.7- and 4.6-fold increases in the probability to reach the MCID in all three outcomes as compared to those in C3-mild (OR&nbsp;=&nbsp;1.72, 95% confidence interval [95% CI]&nbsp;=&nbsp;1.2 - 2.49, p&nbsp;=&nbsp;0.0035 and OR&nbsp;=&nbsp;4.57, 95% CI&nbsp;=&nbsp;2.68 - 7.91, p &lt; 0.0001 respectively). Conclusions: Clustering analysis can identify subpopulations of individuals recovering from ECOPD associated with different responses to PR. Our results may help in defining priority criteria based on the probability of success of PR

Minimal clinically important difference in barthel index dyspnea in patients with COPD

Background: The Barthel Index dyspnea (BId) is responsive to physiological changes and pulmonary rehabilitation in patients with chronic obstructive pulmonary disease (COPD). However, the minimum clinically important difference (MCID) has not been established yet. Aim: To identify the MCID of BId in patients with COPD stratified according to the presence of chronic respiratory failure (CRF) or not. Materials and Methods: Using the Medical Research Council (MRC) score as an anchor, receiver operating characteristic curves and quantile regression were retrospectively evaluated before and after pulmonary rehabilitation in 2327 patients with COPD (1151 of them with CRF). Results: The median post-rehabilitation changes in BId for all patients were −10 (interquartile range = −17 to −3, p&lt;0.001), correlating significantly with changes in MRC (r = 0.57, 95% CI = 0.53 to 0.59, p&lt;0.001). Comparing different methods of assessment, the MCID ranged from −6.5 to −9 points for patients without and −7.5 to −12 points for patients with CRF. Conclusion: The most conservative estimate of the MCID is −9 points in patients with COPD, without and −12 in those with CRF. This estimate may be useful in the clinical interpretation of data, particularly in response to intervention studies

Lack of replication of genetic associations with human longevity

The exceptional longevity of centenarians is due in part to inherited genetic factors, as deduced from data that show that first degree relatives of centenarians live longer and have reduced overall mortality. In recent years, a number of groups have performed genetic association studies on long-living individuals (LLI) and young controls to identify alleles that are either positively or negatively selected in the centenarian population as consequence of a demographic pressure. Many of the reported studies have shown genetic loci associated with longevity. Of these, with the exception of APOE, none have been convincingly reproduced. We validated our populations by typing the APOE locus. In addition, we used 749 American Caucasian LLI, organized in two independent tiers and 355 American Caucasian controls in the attempt to replicate previously published findings. We tested Klotho (KL)-VS variant (rs952706), Cholesteryl Ester Transfer Protein (CETP) I405V (rs5882), Paraoxonase 1 (PON1) Q192R (rs662), Apolipoprotein C-III (APOC3) -641C/A (rs2542052), Microsomal Transfer Protein (MTP) -493G/T (rs2866164) and apolipoprotein E (APOE) epsilon2 and epsilon4 isoforms, (rs7412 and rs429358) haplotypes respectively. Our results show that, at present, except for APOE, none of the selected genes show association with longevity if carefully tested in a large cohort of LLI and their controls, pointing to the need of larger populations for case-control studies in extreme longevity

Association of FOXO3A locus with extreme longevity in the Southern Italian Centenarian Study

A number of potential candidate genes in a variety of biological pathways have been associated with longevity in model organisms. Many of these genes have human homologs and thus have the potential to provide insights into human longevity. Recently, several studies suggested that FOXO3A functions as a key bridge for various signaling pathways that influence aging and longevity. Interestingly, Willcox and colleagues identified several variants that displayed significant genotype-gender interaction in male human longevity. In particular, a nested case-control study was performed in an ethnic Japanese population in Hawaii, and five candidate longevity genes were chosen based on links to the insulin-insulin-like growth factor-1 (IGF-1) signaling pathway. In the Willcox study, the investigated genetic variations (rs2802292, rs2764264, and rs13217795) within the FOXO3A gene were significantly associated with longevity in male centenarians. We validated the association of FOXO3A polymorphisms with extreme longevity in males from the Southern Italian Centenarian Study. Particularly, rs2802288, a proxy of rs2802292, showed the best allelic association-minor allele frequency (MAF) = 0.49; p = 0.003; odds ratio (OR) = 1.51; 95% confidence interval (CI), 1.15-1.98). Furthermore, we undertook a meta-analysis to explore the significance of rs2802292 association with longevity by combining the association results of the current study and the findings coming from the Willcox et al. investigation. Our data point to a key role of FOXO3A in human longevity and confirm the feasibility of the identification of such genes with centenarian-controls studies. Moreover, we hypothesize the susceptibility to the longevity phenotype may well be the result of complex interactions involving genes and environmental factors but also gender

Different molecular mechanisms causing 9p21 deletions in acute lymphoblastic leukemia of childhood

Deletion of chromosome 9p21 is a crucial event for the development of several cancers including acute lymphoblastic leukemia (ALL). Double strand breaks (DSBs) triggering 9p21 deletions in ALL have been reported to occur at a few defined sites by illegitimate action of the V(D)J recombination activating protein complex. We have cloned 23 breakpoint junctions for a total of 46 breakpoints in 17 childhood ALL (9 B- and 8 T-lineages) showing different size deletions at one or both homologous chromosomes 9 to investigate which particular sequences make the region susceptible to interstitial deletion. We found that half of 9p21 deletion breakpoints were mediated by ectopic V(D)J recombination mechanisms whereas the remaining half were associated to repeated sequences, including some with potential for non-B DNA structure formation. Other mechanisms, such as microhomology-mediated repair, that are common in other cancers, play only a very minor role in ALL. Nucleotide insertions at breakpoint junctions and microinversions flanking the breakpoints have been detected at 20/23 and 2/23 breakpoint junctions, respectively, both in the presence of recombination signal sequence (RSS)-like sequences and of other unspecific sequences. The majority of breakpoints were unique except for two cases, both T-ALL, showing identical deletions. Four of the 46 breakpoints coincide with those reported in other cases, thus confirming the presence of recurrent deletion hotspots. Among the six cases with heterozygous 9p deletions, we found that the remaining CDKN2A and CDKN2B alleles were hypermethylated at CpG islands

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03–4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55–5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14–1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37–0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17–1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20–1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45–1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80–13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10–1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32–1.67) and 365 days (RR 1.54, 95%CI 1.21–1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section